GaAs PCM or WAT data to device model using Neural Network to predict device performance and yield and also target and verify device to process centering.
Enhanced Protection Modeling Approach for Power System Transient Stability St...Power System Operation
Accurate protection modelling in power system transient stability studies is required to ensure that reliable conclusions are drawn from such analyses. Typically, protection models available in transient stability programs use only positive sequence quantities such as the positive sequence voltages, currents, etc. to trigger any preventive/corrective actions such as tripping of generators, load-shedding, etc. However, with the increasing penetration of inverter-based resources, these models could prove to be inadequate in some scenarios. The work reported in this paper uses improved modelling practices for protection elements in transient stability studies using sequence/individual phase quantities. This approach does not necessarily require additional data from users and incurs only minimal incremental computational costs. In addition to using the sequence voltages/currents or individual phase voltages/currents for more accurate representation of protection systems, simply monitoring these quantities can also provide useful additional information about the system. Additionally, having access to these quantities could be useful in more accurate modelling of inverter-based resources such as the ability to model converter controls’ protective functions, controls that actively suppress the negative sequence current produced by the inverter, and other such controls that use or control the negative sequence or zero sequence current injections.
A Unified Approach for Performance Degradation Analysis from Transistor to Gat...IJECEIAES
In this paper, we present an extensive analysis of the performance degradation in MOS- FET based circuits. The physical effects that we consider are the random dopant fluctuation (RDF), the oxide thickness fluctuation (OTF) and the Hot-carrier-Instability (HCI). The work that we propose is based on two main key points: First, the performance degradation is studied considering BULK, Silicon-On-Insulator (SOI) and Double Gate (DG) MOSFET technologies. The analysis considers technology nodes from 45nm to 11nm. For the HCI effect we consider also the time-dependent evolution of the parameters of the circuit. Second, the analysis is performed from transistor level to gate level. Models are used to evaluate the variation of transistors key parameters, and how these variation affects performance at gate level as well.The work here presented was obtained using TAMTAMS Web, an open and publicly available framework for analysis of circuits based on transistors. The use of TAMTAMS Web greatly increases the value of this work, given that the analysis can be easily extended and improved in both complexity and depth.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
COVERAGE DRIVEN FUNCTIONAL TESTING ARCHITECTURE FOR PROTOTYPING SYSTEM USING ...VLSICS Design
Time and efforts for functional testing of digital logic is big chunk of overall project cycle in VLSI industry. Progress of functional testing is measured by functional coverage where test-plan defines what needs to be covered, and test-results indicates quality of stimulus. Claiming closer of functional testing requires that functional coverage hits 100% of original test-plan. Depending on the complexity of the design, availability of resources and budget, various methods are used for functional testing. Software simulations using various logic simulators, available from Electronic Design Automation (EDA) companies, is primary method for functional testing. The next level in functional testing is pre-silicon verification using Field Programmable Gate Array (FPGA) prototype and/or emulation platforms for stress testing the Design Under Test (DUT). With all the efforts, the purpose is to gain confidence on maturity of DUT to ensuresfirst time silicon success that meets time to market needs of the industry. For any test-environment the bottleneck, in achieving verification closer, is controllability and observability that is quality of stimulus to unearth issues at early stage and coverage calculation. Software simulation, FPGA prototype, or emulation, each method has its own limitations, be it test-time, ease of use, or cost of software, tools and
hardware-platform. Compared to software simulation, FPGA prototyping and emulation methods pose greater challenges in quality stimulus generation and coverage calculation. Many researchers have identified the problems of bug-detection / localization, but very few have touched the concept of quality stimulus generation that leads to better functional coverage and thereby uncover hidden bugs in FPGA prototype verification setup. This paper presents a novel approach to address above-mentioned issues by embedding synthesizable active-agent and coverage collector into FPGA prototype. The proposed architecture has been experimented for functional and stress testing of Universal Serial Bus (USB) Link Training and Status State Machine (LTSSM) logic module as DUT in FPGA prototype. The proposed solution is fully synthesizable and hence can be used in both software simulation as well as in prototype system. The biggest advantage is plug and play nature of this active-agent component, that allows its reusability in any USB3.0 LTSSM digital core.
This document describes a new method for locating faults in transmission cable lines. It begins by introducing the need for improved cable fault detection technologies. It then describes the development of a novel noncontact sensor (NCS) that can detect electric fields and has adjustable sensitivity through theoretical calculation and simulation. Next, it proposes a new method called FVMD + WVD that uses feedback variational mode decomposition and the Wigner-Ville distribution to more accurately identify the arrival time of fault waves compared to existing methods. Simulations and experiments show that the NCS performs reliably and the new method reduces error in fault location to only 0.48%. The findings demonstrate an improved system for detecting and locating cable faults.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes the evolution of experimental modal analysis techniques for civil engineering structures. It discusses the progression from input-output modal identification, which relies on controlled force excitation and measurement of the response, to output-only modal identification, which analyzes ambient vibration response alone. A variety of equipment for exciting large civil structures is described, from eccentric mass vibrators to servo-hydraulic shakers. The document also provides an overview of common input-output modal identification methods in both the time and frequency domains.
Study of model predictive control using ni lab viewIAEME Publication
This document discusses the implementation of model predictive control (MPC) using National Instruments LabVIEW software. It begins with introductions to MPC and LabVIEW. It then covers constructing state space and transfer function models in LabVIEW. Simulation results are presented for MPC applied to first order systems with and without time delay. MPC performance is compared to PID control, showing MPC can handle constraints and optimize process operation while PID cannot. The document concludes MPC simulation using LabVIEW is successful and simulation results are useful for control system design.
Enhanced Protection Modeling Approach for Power System Transient Stability St...Power System Operation
Accurate protection modelling in power system transient stability studies is required to ensure that reliable conclusions are drawn from such analyses. Typically, protection models available in transient stability programs use only positive sequence quantities such as the positive sequence voltages, currents, etc. to trigger any preventive/corrective actions such as tripping of generators, load-shedding, etc. However, with the increasing penetration of inverter-based resources, these models could prove to be inadequate in some scenarios. The work reported in this paper uses improved modelling practices for protection elements in transient stability studies using sequence/individual phase quantities. This approach does not necessarily require additional data from users and incurs only minimal incremental computational costs. In addition to using the sequence voltages/currents or individual phase voltages/currents for more accurate representation of protection systems, simply monitoring these quantities can also provide useful additional information about the system. Additionally, having access to these quantities could be useful in more accurate modelling of inverter-based resources such as the ability to model converter controls’ protective functions, controls that actively suppress the negative sequence current produced by the inverter, and other such controls that use or control the negative sequence or zero sequence current injections.
A Unified Approach for Performance Degradation Analysis from Transistor to Gat...IJECEIAES
In this paper, we present an extensive analysis of the performance degradation in MOS- FET based circuits. The physical effects that we consider are the random dopant fluctuation (RDF), the oxide thickness fluctuation (OTF) and the Hot-carrier-Instability (HCI). The work that we propose is based on two main key points: First, the performance degradation is studied considering BULK, Silicon-On-Insulator (SOI) and Double Gate (DG) MOSFET technologies. The analysis considers technology nodes from 45nm to 11nm. For the HCI effect we consider also the time-dependent evolution of the parameters of the circuit. Second, the analysis is performed from transistor level to gate level. Models are used to evaluate the variation of transistors key parameters, and how these variation affects performance at gate level as well.The work here presented was obtained using TAMTAMS Web, an open and publicly available framework for analysis of circuits based on transistors. The use of TAMTAMS Web greatly increases the value of this work, given that the analysis can be easily extended and improved in both complexity and depth.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
COVERAGE DRIVEN FUNCTIONAL TESTING ARCHITECTURE FOR PROTOTYPING SYSTEM USING ...VLSICS Design
Time and efforts for functional testing of digital logic is big chunk of overall project cycle in VLSI industry. Progress of functional testing is measured by functional coverage where test-plan defines what needs to be covered, and test-results indicates quality of stimulus. Claiming closer of functional testing requires that functional coverage hits 100% of original test-plan. Depending on the complexity of the design, availability of resources and budget, various methods are used for functional testing. Software simulations using various logic simulators, available from Electronic Design Automation (EDA) companies, is primary method for functional testing. The next level in functional testing is pre-silicon verification using Field Programmable Gate Array (FPGA) prototype and/or emulation platforms for stress testing the Design Under Test (DUT). With all the efforts, the purpose is to gain confidence on maturity of DUT to ensuresfirst time silicon success that meets time to market needs of the industry. For any test-environment the bottleneck, in achieving verification closer, is controllability and observability that is quality of stimulus to unearth issues at early stage and coverage calculation. Software simulation, FPGA prototype, or emulation, each method has its own limitations, be it test-time, ease of use, or cost of software, tools and
hardware-platform. Compared to software simulation, FPGA prototyping and emulation methods pose greater challenges in quality stimulus generation and coverage calculation. Many researchers have identified the problems of bug-detection / localization, but very few have touched the concept of quality stimulus generation that leads to better functional coverage and thereby uncover hidden bugs in FPGA prototype verification setup. This paper presents a novel approach to address above-mentioned issues by embedding synthesizable active-agent and coverage collector into FPGA prototype. The proposed architecture has been experimented for functional and stress testing of Universal Serial Bus (USB) Link Training and Status State Machine (LTSSM) logic module as DUT in FPGA prototype. The proposed solution is fully synthesizable and hence can be used in both software simulation as well as in prototype system. The biggest advantage is plug and play nature of this active-agent component, that allows its reusability in any USB3.0 LTSSM digital core.
This document describes a new method for locating faults in transmission cable lines. It begins by introducing the need for improved cable fault detection technologies. It then describes the development of a novel noncontact sensor (NCS) that can detect electric fields and has adjustable sensitivity through theoretical calculation and simulation. Next, it proposes a new method called FVMD + WVD that uses feedback variational mode decomposition and the Wigner-Ville distribution to more accurately identify the arrival time of fault waves compared to existing methods. Simulations and experiments show that the NCS performs reliably and the new method reduces error in fault location to only 0.48%. The findings demonstrate an improved system for detecting and locating cable faults.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes the evolution of experimental modal analysis techniques for civil engineering structures. It discusses the progression from input-output modal identification, which relies on controlled force excitation and measurement of the response, to output-only modal identification, which analyzes ambient vibration response alone. A variety of equipment for exciting large civil structures is described, from eccentric mass vibrators to servo-hydraulic shakers. The document also provides an overview of common input-output modal identification methods in both the time and frequency domains.
Study of model predictive control using ni lab viewIAEME Publication
This document discusses the implementation of model predictive control (MPC) using National Instruments LabVIEW software. It begins with introductions to MPC and LabVIEW. It then covers constructing state space and transfer function models in LabVIEW. Simulation results are presented for MPC applied to first order systems with and without time delay. MPC performance is compared to PID control, showing MPC can handle constraints and optimize process operation while PID cannot. The document concludes MPC simulation using LabVIEW is successful and simulation results are useful for control system design.
Viscri is a village in Transylvania, Romania that is known for its spectacular and ancient fortified Saxon church that has been designated a UNESCO World Heritage site. The village of 1,000 people gained international attention after Prince Charles purchased a home there. Visitors should try the homemade chicken soup, bread, and jams while taking in the charm that led the Prince of Wales to buy property in the scenic village.
This certificate certifies that Russell Marino successfully completed SYS101 Section 310, a course on Fundamentals of Systems Planning, Research, Development and Engineering, on July 17, 2014.
Impact Over Activity: Why Experimentation is the New Imperative for Scientifi...G3 Communications
From the B2B Content2Conversion Conference, with:
-Brad Gillespie, Vice President of Marketing, SiriusDecisions
-Carrie Rediker, Research Director, Demand Creation Strategies, SiriusDecisions
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Маркетинговая стратегия розничной компании:Как конкурировать с гигантами?Alexander Shubin
Презентация выступления А. Шубина, управляющего директора ПБК Менеджмент (www.myretailstrategy.com) в качестве специального гостя в Русской школе управления на курсе "Директор по рознице". В презентации делается акцент на ключевые факторы развития розничного рынка в России, тренды потребительского рынка, актуальные для средней компании, специфические инструменты разработки маркетинговой стратегии для малой и средней розничной компании для создания устойчивых конкурентных преимущества.
This document discusses hardware design verification and testing techniques. It covers emulation architectures like FPGA-based and processor-based systems. It also discusses formal property verification methods, software formal verification, design for test objectives, chip-level DFT techniques, automatic test pattern generation, and testing techniques for analog/mixed-signal circuits like ADCs, PLLs and oscillators.
REAL TIME ERROR DETECTION IN METAL ARC WELDING PROCESS USING ARTIFICIAL NEURA...IJCI JOURNAL
Quality assurance in production line demands reliable weld joints. Human made errors is a major cause of
faulty production. Promptly Identifying errors in the weld while welding is in progress will decrease the
post inspection cost spent on the welding process. Electrical parameters generated during welding, could
able to characterize the process efficiently. Parameter values are collected using high speed data
acquisition system. Time series analysis tasks such as filtering, pattern recognition etc. are performed over
the collected data. Filtering removes the unwanted noisy signal components and pattern recognition task
segregate error patterns in the time series based upon similarity, which is performed by Self Organized
mapping clustering algorithm. Welder’s quality is thus compared by detecting and counting number of
error patterns appeared in his parametric time series. Moreover, Self Organized mapping algorithm
provides the database in which patterns are segregated into two classes either desirable or undesirable.
Database thus generated is used to train the classification algorithms, and thereby automating the real time
error detection task. Multi Layer Perceptron and Radial basis function are the two classification
algorithms used, and their performance has been compared based on metrics such as specificity, sensitivity,
accuracy and time required in training.
This document compares level 1, 2, and 3 MOSFET models in SPICE simulations. It provides background on device modeling and outlines the key equations that define each model level. Level 1 is the simplest model and does not account for short channel effects. Level 2 includes mobility degradation and threshold voltage variations. Level 3 has similar accuracy to level 2 but faster simulation time and better convergence. Drain current versus drain-source voltage characteristics are plotted to show differences between the models.
IRJET- Comparative Analysis of High Speed SRAM Cell for 90nm CMOS TechnologyIRJET Journal
This document presents a comparative analysis of 6T and 8T SRAM cells for 90nm CMOS technology. It begins with an abstract discussing the simulation of low power SRAM cells at different frequencies. The main body then provides background on SRAM cells, discusses related work analyzing 6T and 8T SRAM cell designs. It presents the architecture and operating principles of an 8T SRAM cell, including write and read modes. Simulation results show the 8T SRAM cell has lower dynamic power consumption than a 6T cell, with readings of 82 micro Watts for read and 120 micro Watts for write. Logic validation testing confirms the 8T cell correctly writes and reads input bit values.
126 a fuzzy rule based approach for islandingLalitha Lalli
The document presents a fuzzy rule-based approach for islanding detection in distributed generation systems.
1) A decision tree is used to determine the most significant features for islanding detection from 11 potential features. The decision tree identifies 3 key features: rate of change of frequency, voltage deviation, and rate of change of power.
2) Trapezoidal fuzzy membership functions are developed based on the decision boundaries of the decision tree for each of the 3 features.
3) A fuzzy rule base is generated using the fuzzy membership functions to classify situations as islanding or non-islanding. Some fuzzy membership functions are merged to simplify the rule base.
SRAM BASED IN-MEMORY MATRIX VECTOR MULTIPLIERIRJET Journal
This document describes an SRAM-based in-memory matrix vector multiplier. It discusses using SRAM cells to perform matrix vector multiplication operations directly in memory. The weights stored in the SRAM cells are converted to analog voltages using a DAC. A switched capacitor circuit then multiplies the analog voltages by a digital input vector. Finally, charge sharing is used to sum the output voltages along each column. The circuit size, power consumption, and calculation time scale linearly with the architecture. Analytical formulas are provided for energy usage. The impact of manufacturing variations on precision is also examined.
Fault diagnosis of a high voltage transmission line using waveform matching a...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
This document summarizes the design of a high-frequency field-programmable analog array (FPAA). Key points:
- The FPAA architecture is based on a regular pattern of identical cells that are locally interconnected for high frequency performance. Programming is achieved by modifying cells' bias conditions digitally, not via switches in the signal path.
- Each cell can perform functions like weighted summing, multiplication, integration, and nonlinear operations like clipping. Cells operate in either a passive mode where analog blocks process signals, or an active mode where a control block provides additional nonlinear functions.
- The locally interconnected architecture restricts connections between cells to improve high frequency performance, while still supporting implementation of classes of circuits like filters
Fault Diagnosis of a High Voltage Transmission Line Using Waveform Matching A...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
FirstEnergy Service Company on behalf of its transmission owning affiliates determined there was a need to improve the accuracy and speed of its fault location process especially for the 138 and 69 kV system. This system is heavily tapped with industrial customers and substations. The main objective of this project was to see if the need to call out staff, usually overnight, to run the fault-location program could be eliminated for the majority of faults that occur in a pilot/test area.
The overall objective of this effort is to reduce the time to determine where a fault has occurred with sufficient certainty to route field crews to the location of the fault quickly and improve restoration times. At some locations, there may be the ability to sectionalize the 69 kV or 138 kV transmission network so that operations staff can begin restoring customers in areas not directly affected by the faulted line section.
This update paper will provide additional details regarding the implementation of the analytics methods and data handling and transformation required to fully automate the process. Furthermore, a recent enhancement in the automated determination of the appropriate fault current to use will be provided. This enhancement more appropriately removes the impact of the DC offset often present within the fault measurement made by the digital fault recorder.
IRJET- A Literature Study on Fault Recognition in Different SystemIRJET Journal
This document summarizes several papers on fault recognition techniques in power systems. It discusses different fault recognition methods that have been studied, including the use of SCADA systems, PLCs, stochastic programming models, fuzzy logic with discrete wavelet transforms, and microprocessor differential relays. The key conclusions from the papers are that automation techniques can improve reliability and power quality, PLC-based systems allow more widespread substation automation, stochastic programming is effective for dealing with uncertainties in fault detection, and fuzzy logic with wavelet transforms can accurately classify ten different fault types under varying power system conditions.
The rapid growths of portable electronic devices are increased and they are designing with low power and high speed is critical. To design a three input XOR and XNOR gates using the systematic cell design methodology can be achieved by implementing transmission gate. By this type of designing the low power and high speed can achieved. This architecture is used to maintain summation results for after completing addition process. XOR/XNOR circuits are proposed with high driving capability, full-balanced full-swing outputs and low number transistors of basic structure, high performance and operating at low voltages. This simulation is carried out using TSMC 90nmCMOS technology in Tanner EDA Tool.
Study and Analysis of Low Power SRAM Memory Array at nano-scaled TechnologyIRJET Journal
This document summarizes a study analyzing the design of low-power SRAM memory arrays at nano-scaled CMOS technology. The study adapts a multi-threshold CMOS design to create a novel low-power 6T SRAM cell that can reduce power usage and access time by using transistors with different threshold voltages. Simulation results show that leakage power can be significantly reduced in the idle state, lowering overall power consumption. Various SRAM cell designs are reviewed and a 1KB SRAM memory array is implemented using the proposed low-power 6T SRAM cell to validate the approach.
Optimization of Empirical Modelling of Advanced Highly Strained In 0.7 Ga 0.3...IJECEIAES
An optimized empirical modelling for a 0.25µm gate length of highly strained channel of an InP-based pseudomorphic high electron mobility transistor (pHEMT) using InGaAs–InAlAs material systems is presented. An accurate procedure for extraction is described and tested using the pHEMT measured dataset of I-V characteristics and related multi-bias s-parameters over 20GHz frequency range. The extraction of linear and nonlinear parameters from the small signal and large signal pHEMT equivalent model are performed in ADS. The optimized DC and S-parameter model for the pHEMT device provides a basis for active device selection in the MMIC low noise amplifier circuit designs.
Mobile Systems
Instructor: Mark Bohr, Intel Corp.
• Technology trend and key challenges.
• Underlying system/application requirements.
• Interaction between circuit design and technology: transistor, interconnects, memory.
Integrated Power Electronics
Instructor: Tomas Palacios, MIT
• Technology trend and key challenges.
• Underlying system/application requirements.
• Interaction between circuit design and technology: transistor, interconnects, passive.
Memory Technologies
Instructor: Mark Johnson, Micron Technology
• Technology trend and key challenges.
• Underlying system/application requirements.
• Interaction between circuit design and technology: transistor, interconnects, passive.
Design for
Viscri is a village in Transylvania, Romania that is known for its spectacular and ancient fortified Saxon church that has been designated a UNESCO World Heritage site. The village of 1,000 people gained international attention after Prince Charles purchased a home there. Visitors should try the homemade chicken soup, bread, and jams while taking in the charm that led the Prince of Wales to buy property in the scenic village.
This certificate certifies that Russell Marino successfully completed SYS101 Section 310, a course on Fundamentals of Systems Planning, Research, Development and Engineering, on July 17, 2014.
Impact Over Activity: Why Experimentation is the New Imperative for Scientifi...G3 Communications
From the B2B Content2Conversion Conference, with:
-Brad Gillespie, Vice President of Marketing, SiriusDecisions
-Carrie Rediker, Research Director, Demand Creation Strategies, SiriusDecisions
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Маркетинговая стратегия розничной компании:Как конкурировать с гигантами?Alexander Shubin
Презентация выступления А. Шубина, управляющего директора ПБК Менеджмент (www.myretailstrategy.com) в качестве специального гостя в Русской школе управления на курсе "Директор по рознице". В презентации делается акцент на ключевые факторы развития розничного рынка в России, тренды потребительского рынка, актуальные для средней компании, специфические инструменты разработки маркетинговой стратегии для малой и средней розничной компании для создания устойчивых конкурентных преимущества.
This document discusses hardware design verification and testing techniques. It covers emulation architectures like FPGA-based and processor-based systems. It also discusses formal property verification methods, software formal verification, design for test objectives, chip-level DFT techniques, automatic test pattern generation, and testing techniques for analog/mixed-signal circuits like ADCs, PLLs and oscillators.
REAL TIME ERROR DETECTION IN METAL ARC WELDING PROCESS USING ARTIFICIAL NEURA...IJCI JOURNAL
Quality assurance in production line demands reliable weld joints. Human made errors is a major cause of
faulty production. Promptly Identifying errors in the weld while welding is in progress will decrease the
post inspection cost spent on the welding process. Electrical parameters generated during welding, could
able to characterize the process efficiently. Parameter values are collected using high speed data
acquisition system. Time series analysis tasks such as filtering, pattern recognition etc. are performed over
the collected data. Filtering removes the unwanted noisy signal components and pattern recognition task
segregate error patterns in the time series based upon similarity, which is performed by Self Organized
mapping clustering algorithm. Welder’s quality is thus compared by detecting and counting number of
error patterns appeared in his parametric time series. Moreover, Self Organized mapping algorithm
provides the database in which patterns are segregated into two classes either desirable or undesirable.
Database thus generated is used to train the classification algorithms, and thereby automating the real time
error detection task. Multi Layer Perceptron and Radial basis function are the two classification
algorithms used, and their performance has been compared based on metrics such as specificity, sensitivity,
accuracy and time required in training.
This document compares level 1, 2, and 3 MOSFET models in SPICE simulations. It provides background on device modeling and outlines the key equations that define each model level. Level 1 is the simplest model and does not account for short channel effects. Level 2 includes mobility degradation and threshold voltage variations. Level 3 has similar accuracy to level 2 but faster simulation time and better convergence. Drain current versus drain-source voltage characteristics are plotted to show differences between the models.
IRJET- Comparative Analysis of High Speed SRAM Cell for 90nm CMOS TechnologyIRJET Journal
This document presents a comparative analysis of 6T and 8T SRAM cells for 90nm CMOS technology. It begins with an abstract discussing the simulation of low power SRAM cells at different frequencies. The main body then provides background on SRAM cells, discusses related work analyzing 6T and 8T SRAM cell designs. It presents the architecture and operating principles of an 8T SRAM cell, including write and read modes. Simulation results show the 8T SRAM cell has lower dynamic power consumption than a 6T cell, with readings of 82 micro Watts for read and 120 micro Watts for write. Logic validation testing confirms the 8T cell correctly writes and reads input bit values.
126 a fuzzy rule based approach for islandingLalitha Lalli
The document presents a fuzzy rule-based approach for islanding detection in distributed generation systems.
1) A decision tree is used to determine the most significant features for islanding detection from 11 potential features. The decision tree identifies 3 key features: rate of change of frequency, voltage deviation, and rate of change of power.
2) Trapezoidal fuzzy membership functions are developed based on the decision boundaries of the decision tree for each of the 3 features.
3) A fuzzy rule base is generated using the fuzzy membership functions to classify situations as islanding or non-islanding. Some fuzzy membership functions are merged to simplify the rule base.
SRAM BASED IN-MEMORY MATRIX VECTOR MULTIPLIERIRJET Journal
This document describes an SRAM-based in-memory matrix vector multiplier. It discusses using SRAM cells to perform matrix vector multiplication operations directly in memory. The weights stored in the SRAM cells are converted to analog voltages using a DAC. A switched capacitor circuit then multiplies the analog voltages by a digital input vector. Finally, charge sharing is used to sum the output voltages along each column. The circuit size, power consumption, and calculation time scale linearly with the architecture. Analytical formulas are provided for energy usage. The impact of manufacturing variations on precision is also examined.
Fault diagnosis of a high voltage transmission line using waveform matching a...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
This document summarizes the design of a high-frequency field-programmable analog array (FPAA). Key points:
- The FPAA architecture is based on a regular pattern of identical cells that are locally interconnected for high frequency performance. Programming is achieved by modifying cells' bias conditions digitally, not via switches in the signal path.
- Each cell can perform functions like weighted summing, multiplication, integration, and nonlinear operations like clipping. Cells operate in either a passive mode where analog blocks process signals, or an active mode where a control block provides additional nonlinear functions.
- The locally interconnected architecture restricts connections between cells to improve high frequency performance, while still supporting implementation of classes of circuits like filters
Fault Diagnosis of a High Voltage Transmission Line Using Waveform Matching A...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
FirstEnergy Service Company on behalf of its transmission owning affiliates determined there was a need to improve the accuracy and speed of its fault location process especially for the 138 and 69 kV system. This system is heavily tapped with industrial customers and substations. The main objective of this project was to see if the need to call out staff, usually overnight, to run the fault-location program could be eliminated for the majority of faults that occur in a pilot/test area.
The overall objective of this effort is to reduce the time to determine where a fault has occurred with sufficient certainty to route field crews to the location of the fault quickly and improve restoration times. At some locations, there may be the ability to sectionalize the 69 kV or 138 kV transmission network so that operations staff can begin restoring customers in areas not directly affected by the faulted line section.
This update paper will provide additional details regarding the implementation of the analytics methods and data handling and transformation required to fully automate the process. Furthermore, a recent enhancement in the automated determination of the appropriate fault current to use will be provided. This enhancement more appropriately removes the impact of the DC offset often present within the fault measurement made by the digital fault recorder.
IRJET- A Literature Study on Fault Recognition in Different SystemIRJET Journal
This document summarizes several papers on fault recognition techniques in power systems. It discusses different fault recognition methods that have been studied, including the use of SCADA systems, PLCs, stochastic programming models, fuzzy logic with discrete wavelet transforms, and microprocessor differential relays. The key conclusions from the papers are that automation techniques can improve reliability and power quality, PLC-based systems allow more widespread substation automation, stochastic programming is effective for dealing with uncertainties in fault detection, and fuzzy logic with wavelet transforms can accurately classify ten different fault types under varying power system conditions.
The rapid growths of portable electronic devices are increased and they are designing with low power and high speed is critical. To design a three input XOR and XNOR gates using the systematic cell design methodology can be achieved by implementing transmission gate. By this type of designing the low power and high speed can achieved. This architecture is used to maintain summation results for after completing addition process. XOR/XNOR circuits are proposed with high driving capability, full-balanced full-swing outputs and low number transistors of basic structure, high performance and operating at low voltages. This simulation is carried out using TSMC 90nmCMOS technology in Tanner EDA Tool.
Study and Analysis of Low Power SRAM Memory Array at nano-scaled TechnologyIRJET Journal
This document summarizes a study analyzing the design of low-power SRAM memory arrays at nano-scaled CMOS technology. The study adapts a multi-threshold CMOS design to create a novel low-power 6T SRAM cell that can reduce power usage and access time by using transistors with different threshold voltages. Simulation results show that leakage power can be significantly reduced in the idle state, lowering overall power consumption. Various SRAM cell designs are reviewed and a 1KB SRAM memory array is implemented using the proposed low-power 6T SRAM cell to validate the approach.
Optimization of Empirical Modelling of Advanced Highly Strained In 0.7 Ga 0.3...IJECEIAES
An optimized empirical modelling for a 0.25µm gate length of highly strained channel of an InP-based pseudomorphic high electron mobility transistor (pHEMT) using InGaAs–InAlAs material systems is presented. An accurate procedure for extraction is described and tested using the pHEMT measured dataset of I-V characteristics and related multi-bias s-parameters over 20GHz frequency range. The extraction of linear and nonlinear parameters from the small signal and large signal pHEMT equivalent model are performed in ADS. The optimized DC and S-parameter model for the pHEMT device provides a basis for active device selection in the MMIC low noise amplifier circuit designs.
Mobile Systems
Instructor: Mark Bohr, Intel Corp.
• Technology trend and key challenges.
• Underlying system/application requirements.
• Interaction between circuit design and technology: transistor, interconnects, memory.
Integrated Power Electronics
Instructor: Tomas Palacios, MIT
• Technology trend and key challenges.
• Underlying system/application requirements.
• Interaction between circuit design and technology: transistor, interconnects, passive.
Memory Technologies
Instructor: Mark Johnson, Micron Technology
• Technology trend and key challenges.
• Underlying system/application requirements.
• Interaction between circuit design and technology: transistor, interconnects, passive.
Design for
This document discusses connector models and their accuracy. It begins by describing the evolution of connector models from simple lumped element models to complex multiport microwave models as data rates and simulation capabilities increased. The document then examines extracting connector models from both simulation and measurement, noting sources of variation. Simulation factors like mesh density, material properties, and port setup that impact model accuracy are evaluated. Measurement challenges like fixture removal calibration assumptions and footprint differences that can introduce errors are also discussed. The impacts of real world mechanical variations like insertion depth and solder variations that are often ignored are highlighted. Overall, the document aims to analyze the accuracy of connector models and highlight sources of potential inaccuracies.
1) A large-scale stochastic automotive crash simulation was performed using 128 parallel simulations on a Cray T3E supercomputer. This allowed analysis of the statistical effects of uncertainties in vehicle properties and crash conditions.
2) Results showed the deterministic single-point analyses produced conservative designs and did not capture the most likely responses. Intrusion values from stochastic analysis had higher means and different most probable values than the deterministic analyses.
3) The impact angle had a large influence on responses like intrusion based on scatter plots, showing a chaotic relationship and inability to control intrusions through angle variation. The stochastic analysis provided more insight than deterministic analysis alone.
Laird Snowden has over 25 years of experience in technology and engineering, currently working as Director at New Technology Expediting Assistance which specializes in technology development. He is an expert in semiconductors, electronics, mixed signal development, and new technologies. Snowden has successfully led projects to develop new semiconductor fabrication processes and products. He aims to introduce new technologies and solve problems where others have failed, applying principles like Occam's razor.
This document proposes a healing program for chronic homeless individuals. It discusses the current situation for many homeless people, including living on the streets, lack of access to healthcare, ID issues, and run-ins with law enforcement. It advocates creating a self-sustaining community where homeless individuals can heal physically, emotionally and spiritually by caring for unwanted animals. Quotes from the Bible emphasize helping the least among us and treating others with compassion.
Combining the best of Christian programs in a rural farm setting, where people can have 24 hour access to good honest work with animals and the soil, flowers and art, thus supplying a need to be needed.. In addition having chapel and a message of love, healing and forgiveness. Thus supplying a spiritual need. Having unwanted animals for them to care for and an animal hospice to restore animals otherwise about to be put down, thus supplying low risk effective level of healing in nurture and love in caring for unwanted animals, healing each other, giving and receiving pure love.
This document discusses establishing a healing program for the chronically homeless. It describes the difficult lives homeless people live without basic necessities and support systems. It advocates creating a self-sustaining community where homeless people and unwanted animals can heal each other through love, care, and finding purpose. The document references Bible verses about helping the least among us and calls readers to compassionately help the homeless at their doorsteps rather than ignore their suffering.
This document provides a summary of Laird Snowden's experience and qualifications. It includes contact information, a performance summary, and lists membership in professional organizations. The bulk of the document describes Snowden's extensive experience in semiconductor testing and new product introduction, including bringing fabrication facilities online, developing automated test equipment, improving yield, and reducing test time. It highlights experience at companies including Bell Labs, AT&T, TriQuint, and Silicon Labs.
Laird Snowden led a team that successfully brought up a $1B GaAs fab within 90 days at Tri-Quint. He has extensive experience in NPI testing and validation, product design, semiconductor fab bring up, and moving from planning to full high-volume offshore production. He identified and fixed a giant profit hole in one of his business units that turned it from a loss to a 50% return on $500M in gross sales.
This test executive automatically configures itself when running production tests and creates custom parts from an order database by lot number. It also includes engineering silicon validation tests written to verify device performance with design groups, which are operated by software switches in engineering mode for faster testing without reading excel pragmas.
This is the Mixed signal digital test pattern macro i created. It creates hundreds of thousands of commented lines of test patterns for comparing pass fail, capturing data and making analog measurements in a matter of seconds from only a few hundred lines of my opcode/operand command sets, which cover all tyypes of patterns and die communication protocols.
This saves weeks of coding, produces good pattern compiles immediately, creates a record and good for debug with design.
Laird Snowden provides a sampling of his certificates, awards, and independent coursework. He is a member of IEEE and the Association of Old Crows. He received performance stock options from AT&T and RSU grants from Silicon Labs. His coursework includes statistical analysis, process improvement, data modeling, RF modeling, S-parameter measurements, analog measurements, marketing, business case analysis, quality assurance, and programming languages like C++, Perl, UNIX, AWK/SED, TCL, and SQL. He also took courses in Splus, neural network data modeling, non-linear data analysis, semiconductor failure analysis, electro-migration failures, photographic data graphing, and PCB design.
This is the system i developed a ceramic filter for which had been causing it to fail. This SRM Electronic warfare system caused all of the Sam missiles to miss our pilots in the Gulf war
The document provides recommendations for Laird R. Snowden Jr. from several individuals who worked with him. They describe his experience developing testing strategies and characterizing semiconductor devices at companies including Silicon Labs, Bell Labs, and Allegro Microsystems. Recommenders highlight his problem solving skills, attention to detail, and ability to tackle difficult projects. They recommend him for engineering roles involving test development, characterization, modeling, and data analysis.
This is the Mixed signal digital test pattern macro i created. It creates hundreds of thousands of commented lines of test patterns for comparing pass fail, capturing data and making analog measurements in a matter of seconds from only a few hundred lines of my opcode/operand command sets, which cover all tyypes of patterns and die communication protocols.
This saves weeks of coding, produces good pattern compiles immediately, creates a record and good for debug with design.
First membrane probe card lsnowden cascade microtechLaird Snowden
Lsnowden: Worlds first membrance probe card i developed with Cascade Microtech for the AT&T longlines OC48 CDR Porobe test which i also built with RnS instruments. I wrote testes and test executive, data crons, prober control driver and logic, i also wrote my on statistics report generator software in the test executive.
The document discusses the development of a test solution for SONET TTRN/TRCV devices operating at 2.5 Gbps. A hybrid approach is proposed that uses an LTX Fusion HF ATE platform with additional rack-mounted RF equipment. The solution includes a membrane probe card and custom fixture board to enable high-speed RF and digital testing of the devices to validate performance at high data rates and temperatures.
The document discusses using process control monitor (PCM) data from wafer fabrication to predict device performance and wafer yield. PCM data from various sites on the wafer are collected during fabrication and correlated with performance data from devices near those sites. A predictive model is created using the PCM data as inputs to predict device parameters and yield as outputs. The model allows early prediction of wafer and device quality before full testing. Neural networks and linear models were tested, with neural networks showing slightly better prediction accuracy. The model was deployed using a database and scripting to efficiently predict performance for new wafers based on their PCM data.
The document discusses the development of a test solution for SONET TTRN/TRCV devices operating at 2.5 Gbps. A hybrid approach is proposed that uses an LTX Fusion HF ATE platform with additional rack-mounted RF equipment. The solution includes a membrane probe card and custom fixture board to enable high-speed RF and digital testing of the devices to validate performance at high data rates and temperatures.
Laird Snowden is an experienced semiconductor development and test engineer recommended by multiple former colleagues and managers. He has extensive experience developing testing strategies and solving difficult problems for new semiconductor products. References praise his skills in diagnostics, problem-solving, and attention to detail. They note he is easy to work with and contributes significantly to production test solutions.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
UNLOCKING HEALTHCARE 4.0: NAVIGATING CRITICAL SUCCESS FACTORS FOR EFFECTIVE I...amsjournal
The Fourth Industrial Revolution is transforming industries, including healthcare, by integrating digital,
physical, and biological technologies. This study examines the integration of 4.0 technologies into
healthcare, identifying success factors and challenges through interviews with 70 stakeholders from 33
countries. Healthcare is evolving significantly, with varied objectives across nations aiming to improve
population health. The study explores stakeholders' perceptions on critical success factors, identifying
challenges such as insufficiently trained personnel, organizational silos, and structural barriers to data
exchange. Facilitators for integration include cost reduction initiatives and interoperability policies.
Technologies like IoT, Big Data, AI, Machine Learning, and robotics enhance diagnostics, treatment
precision, and real-time monitoring, reducing errors and optimizing resource utilization. Automation
improves employee satisfaction and patient care, while Blockchain and telemedicine drive cost reductions.
Successful integration requires skilled professionals and supportive policies, promising efficient resource
use, lower error rates, and accelerated processes, leading to optimized global healthcare outcomes.
UNLOCKING HEALTHCARE 4.0: NAVIGATING CRITICAL SUCCESS FACTORS FOR EFFECTIVE I...
Pcm to device_model
1. MICROELECTRONICS READING FACILITY
Subject Updated paper from 1995/6 presented at MTTS test conference Group: HSPL
Date: June 21, 2001
From: Laird R. Snowden, Jr.
Loc: 20A313C, Bldg 20
Dept: 50N6K9100
Ext: 3134
Email: lsnowden@agere.com
PROCESSCONTROLMONITORTODEVICEDATACORRELATION
For Long Haul Sonet Codes
Laird R. Snowden, Jr.
AT&TMicroelectronics
GaAs Wafer Test
ABSTRACT
PCM to device modeling is used as an aid in selecting wafers for testing to meet high throughput
demands, check for design centering and to facilitate lot starts to meet demand based on early
information provided from PCM testing of the wafer during wafer fabrication. Additionally it is
used as an FMA flag, for wafers that have low yield when they are predicted to have high yield.
Wafers have limits on PCM parameters, it was found, however, that these limits did not always
guarantee a good wafer.
Typically, designs are created with a nominal FET or Transistor model that is representative of
the process. After the design is completed, there is no guarantee that every wafer produced in the
process will match the model used for the device design. Here in lies a conundrum. What
happens when wafers come in with little or no yield? What parameter in the process is causing
the devices to fail? What is needed is a model, which can accept as input the outputs of PCM RF
and DC testing and forecast device parameters such as gain.
When HSPL wafers arrive from the foundry, they are typically either fully RF device tested or
sample tested for Incoming Inspection. In the event that the yield is low, the question is asked
“what is wrong? “ Is it the test set ? Are the wafers bad? Is the design centered ? Were there
mechanical problems (metalization etc.), Are all the wafers bad ?. It would be useful to have a
predictive model which accepted as inputs, PCM values.
GaAs models tend to be empirical in nature (other than diode drop). In the event that the PCM
data shipped with the wafer contain all of the inputs required by the design simulator, then they
may be used to predict device performance. This is usually not the case however.
2. Another way to create a predictive model of the device, is to create a model from PCM data and
device data. In this instance data from the RF and DC PCM files is spliced together with data
from the device sites that surround a PCM site. If the electrical characteristics of the device are
dependent on the electrical characteristics of its building blocks (transistors, reistors, capacitors
and inductors) then there should be a strong correlation. However, no one parameter may fully
explain the device performance. One PCM parameter can be offset be another. An example is
variation in sheet rho combined with a deviation (from the model used in the circuit design) in
gate length. A model can be expected to work better if it includes all of the PCM properties
(independent variables) which can uniquely cause the designed device to change in performance
(dependant variables).
Many of the PCM measurements are non-orthogonal. An example is Gm at 1ma/mm, Gm at 20
ma/mm and Gm at 50 ma/mm. A draftsman plot of these grouped values showing Gm10 Vs
Gm20 may be on an identical slope except for some devices in the tail. It is important to include
the tail information since this becomes significant if multiplied by many parameters.
The first step is to select devices surrounding a PCM site. Devices that may have mechanical
failures (measured values that are outside the probability error lines ) should be excluded along
with catastrophic failures. The ratio should be noted for further study into defect density if it is a
significant yield loss.
There are several considerations:
1) Devices may be bad as the result of mechanical failures in the process. Examples of this are
open vias, shorted traces (metal lift-off). Failures of this type tend to be catastrophic.
2) Devices may fail due to variances in the electrical characteristics of the devices. These are
determined by the components from which the device is built such as transistors, resistors,
inductors and capacitors. The characteristics of these constituent components are quantified
in PCM (Process Control Monitor) testing and include such things as barrier height, RF Gm
(extrinsic) Gm(intrinsic(calculated from measured values), Ft, GMdc at different current
densities, RF Rds, Vt, Idss, Cgs and so on. Additionally, variation in metalization can have
an effect on the PCM RF values, an example is gate width and gate length. Shorter gate
lengths could cause Gm to go down and Ft to go up. This indicates that process changes can
move more than one parameter. It also impies that there can be an associated ratio of
change between the individual parameters for a given process variation. The resultant
effects can therefore be non-linear and include crossing and non-crossing first order and
second order interactions. An interaction is the event of one independent variable modifying
the effect of a second independent variable on the dependant variable.
It is important to differentiate between parametric mechanical failures since these tend to be
uncorrelated to PCM measurements such as Gm, Rds etc..
Electrical parametric failures for long haul Sonet codes are such things as low gain where the
limits are placed on a continuous distribution and frequently bisect the distribution on designs
that are operating at or close to the theoretical limits of the process, or in some instances, of
physics (i.e. noise contribution). Frequently gas flow patterns over the wafer during processing
or epitaxial growth can seen in the electrical characteristics of a test FET if included in the
3. primary site with PCM measurements included in the primary test program. They may also
sometimes be seen in a 3D surface plot of promary device measurement (x,y position versus
paramter). These variations cause device performance to vary over the wafer. A process under
control tends to indicate a flat distribution with a steep roll off at the sides, however, there is risk
in assuming this without examining data.
Mechanical failures tend to be discontinuous catastrophic failures due to open vias, filaments,
dielectric punch through, incomplete metal lift off, etc. They can be systemic (mask scratches
that repeat in the same place in each reticle, area dependant (more prone in the outside ring) or
random. Yo/Do analysis is useful in quantifying this where it is a strong contributor to yield
loss..
What is needed is a model that can indicate from thePCM data that is shipped with each wafer, the
expected yield of a wafer (percent good) and the robustness of a wafer ( how far away from the
test limits the critical device parameters are).
Device RF performance and wafer yield is predicted from models using wafer PCM (Process
Control Monitor) data which is obtained during the fabrication of the wafer. RF device wafer
probe data is obtained using high speed production probe cards that equal or exceed package
performance. Modeling is done with Neural Network non-linear modeling and or Exceltm
linear
modeling. Non-linear data transformations may be included in the Excel model where they are
known. The AT&T ME GaAs LG1605DXB Limiting Amp was chosen for this study because it
has high gain and wide bandwidth (4 GHz). The two sets of data are combined in a relational
database and an empirical model is created. The PCM data is represented as the independent
variables in the model and the device data as the dependant variable. In a neural network model,
multiple inputs (independent variables) can be used and single or multiple outputs (dependant
variables) can be modeled. The accuracy of the model is greater if multiple single device output
models are created rather than one multiple output model. This is due to the risk of overtraining
one or more variables (with stronger correlation) in a multiple output model.
Software implemented Neuaral Network models are better at averaging out noise and dealing
with non-orthogonal data than algebraic models. They also do a better job extrapolating data
outside the experience range of the training set data used during creation of the model. Some
types of NN’s can be self training, continuous training as well .
Advantages of Neural networks:
1) Can deal with non-orthogonal data which includes information in the tail distributions.
2) Good at averaging out noise in measurements
3) Offers both linear and Nth order non-linear modelling even where there is no knowledge of
the non-linear relationships, handled automatically by 2nd
layer nodes.
4) Do not fail catastrophically when encounter data which is outside of the training set. The
model extrapolates along a sigmoidal transfere curve (assuming a sigmoidal transfere
function was used, which is most common). The best way to describe this is a ‘conservative
trend estimate’.
5) Models crossing and non-crossing interactions od independent variables.
4. Afetr creation, the model was deployed on a UNIXTM
server which also contained an InformixTM
relational database for PCM, Wafer and Package data when this work was done in 1995. A
calibration standard was implemented for the probe card with the same pad frame as the device
on a GaAs wafers.
I wrote a Unix script to parse the data and extract it to a flat file. The variables then ran through
an AWK script which contained the model to generate the predicted variable. The data was then
run through an S Plus script and an ACE report writer script to generate the plots.
The model is used by entering a wafer number, the script then gets the PCM data for that wafer
from the database and predicts device RF performance for each PCM site for which data exists.
The PCM data consists of active DC and RF and passive device data.
Results indicate good performance of the model with predicted gain closely matching measured
gain for different wafers with varying PCM distributions.
WAFER LAYOUT
Devices are fabricated on GaAs HFET Transistors. The wafer is laid out in reticules, each reticule has one or more
type of devices and one PCM site. The reticule is then stepped across the wafer. The PCM sites can thus provide
contour maps of the wafer surface. PCM sites contain active and passive devices, FETs, resistors, capacitors,
interconnect testers etc.. The FETs are measured at various points during fabrication. DC data is taken at Ohmic,
Final data is measured at completion and consists of both RF and DC measurements. The PCM data contains a row
and column identifier for each reticule. The devices are also identified by row and column number. No actual
numbers are placed on the devices, PCB’s or reticules. The numbers are virtual and are defined by a consistent
starting point on the wafer. Note that the new Texas TQS foundry includes clown numbers unlike the TQS Oregon
facility which declined my request to add clown numbers.
DATA
PCM PCM PCM PCM PCM
PCM PCM PCM PCM PCM
PCM PCM PCM PCM PCM
PCM PCM PCM PCM PCM
PCM PCM PCM PCM PCM
PCM PCM
PCM PCM PCM
PCM
PCM
PCM
PCM
PCM
PCM
PCM
device device device device
device device device device
device device device device
device device device
0,0
row,col
row,col
PCM
device device device device
device device device device
device device device device
device device device
RETICLE
5. The data is stored in a common relational database in three tables, being: PCM, Wafer and Package. The wafer
device data is identified by its row and column number. A virtual die number is created from the row and column
identity ( die number 114 would be located in row 1, column 14). A preprocessing algorithm uses the row and
column number to generate a reticule identity that matches the PCM identity, this reticule identity is added to the
wafer device data. The PCM and device data is spliced together using the common reticule identity and unloaded to
a new flat file for use in modeling. An example of an algorithm for extracting reticule number identity is:
VIRTUAL RETICULE CALCULATION:
xret= (die_num mod 100) div 5) - offset
yret= offset-(die_num div 100) div 5)
100 is used for 2 digit row and column designations, 5 is used for a 5 x 5 place row and column reticule.
Note:
Div and Mod are HP Basic commands that can be implemented in other programming languages with the Integer
command or in C by casting a float to an integer as follows:
<num> div <100> is equivalent to int(<num>/<100>)
<num> mod <100> is equivalent to <num> - (int(<num>/<100.) * <100>)
100 is used for up to 4 digit wafer numbers (2 digit row and 2 digit column numbers).
1000 would be used for up to 6 difit wafer numbers ( 3 digit row and 3 digit column numbers).
As an example 214 mod 100 = 14
214 div 100 = 2
DATABASE SPLICING SQL EXAMPLE:
SELECT pcm_table.wafer_num, pcm_table.param1, wafer_table.xret, wafer_table.yret, wafer_table.die_num,
wafer_table.param1
FROM pcm_table, wafer_table
WHERE pcm_table.wafer_num=“12345” and pcm_table.wafer_num=wafer_table.wafer_num and
pcm_table.xret=wafer_table.xret and pcm_table.yret=wafer_table.yret
SPLICED FLAT FILE EXAMPLE:
Wafer No PCM param1 x
reticule
y
reticule
die number wafer param1
987654 123.1 1 1 101 234.1
987654 123.1 1 1 102 234.2
987654 123.1 1 1 103 234.3
987654 123.1 1 1 104 234.4
987654 123.2 2 1 201 234.5
987654 123.2 2 1 202 234.6
987654 123.2 2 1 203 234.7
987654 123.2 2 1 204 234.8
Please note that this assumes that virtual reticle ID numbers are created and stored with test data to be matched to the
PCM ID numbers. The center of the reticle is the best location for alignment and focus and is reserved for primary
devices on the physical construct, but no such limitiations exist in the virtual.
6. An alternate way to do this would be to take the PCM die number and pick out the surrounding die numbers. Virtual
reticle ID numbers would need to contain an offset to center the PCM site in the reticle. The PCM site is usually
placed in the corner of the rteticle.
An example of this would be:
Selection SQL statement:
Where x.pcmtable>= (dienum.devicetable MOD 100) AND x.pcmtable<= (dienum.devicetable MOD 100)+1 AND
y.pcmtable>=(dienum.devicetable DIV 100)-1 AND y.pcmtable<=(dienum.devicetable DIV 100)
selects sites 401, 402 and 502 and PCM site 501.
MODELING
The independent variables for the model are the PCM RF, DC and passive component measurements. In the latest
model deployed, there are seven DC FET parameters Pinchoff, transconductance at various channel currents, etc),
two RF and one passive measurement. The dependent variables can be device RF and or DC measured data and or
yield. In the deployment described there was only one parameter (RF gain) which, empirically, was found to be the
key parameter for device yield. Modeling has been done with both multiple linear and non-linear Neural Network
modeling. The neural network has the advantage of being able to model non-linear interactions between dependent
variables as well as the primary non-linear responses, if they exist. In addition, Neural network models are not
impaired by non-orthogonal data.
The Neural network showed a 93% correlation coefficient, the Excel model indicated a 90 % correlation coefficient.
Since the Neural Network model (Predict by Neural Ware) was still in Beta site testing, the model extraction feature
was not yet fully implemented and the initial model was deployed using Excel, which has performed well.
1 2 3 4 5 6 7 8
1
die
number
101
die
number
102
die
number
103
die
number
104
die
number
105
die
number
106
die
number
107
die
number
108
2
die
number
201
die
number
202
die
number
203
die
number
204
die
number
205
die
number
206
die
number
207
die
number
208
3
die
number
301
die
number
302
die
number
303
die
number
304
die
number
305
die
number
306
die
number
307
die
number
308
4
die
number
401
die
number
402
die
number
403
die
number
404
die
number
405
die
number
406
die
number
407
die
number
408
5
PCM
number
501
die
number
502
die
number
503
die
number
504
die
number
505
die
number
506
die
number
507
die
number50
8
7. Untrained Neural Network model:
The underlying assumption in using this technique is th ability to relate PCM and device
measurements. The best place to do this is at the wafer level. Therefore it becomes
necessary to make accurate RF measurements on wafer. The following section deals with
the early development of RF Wafer probe.
STANDARDS
Two different standards were implemented. A standard was built on GaAs using coplanar waveguide with Air-
Bridge crossovers to insure the ground planes on either side are electrically tied together and equalized, 50 ohm load
and open and short sites. The pad frame of the standard matches the pad frame of the device. This standard was
characterized by Cascade Microtech and found to perform well. These were used to calibrate the S Parameter
analyzer.
8. EXAMPLE OF GaAs THROUGH CALIBRATION STANDARD. Open, short and 50 ohm standards were also
included.
The GaAs coplanar waveguide calibration through standard
provides adequate probe verification at 2.5 Gb/s. The combined
probe card and through insertion loss for both Cascade and GGB
cards was less than 1/2 dB at 2.5 Gb/s.
9. RF WAFER DATA COLLECTION
CASCADE MEMBRANE CARD GGB PROBE CARD
High Speed probe cards have been used, using both Membrane technology and
coaxial probe tips, both have worked excellently with performance equal to or
better than package performance for , in this case, a limiting amplifier with up to
45 dB of gain and bandwidth up to 4 GHz (8 Gb/s). The devices remained stable.
These probe cards allow accurate selection of good devices, facilitate modeling
(wafer device to PCM provides a broader selection of data, includes low gain
‘failures’ in the model and eliminates the need to track individual die identity
through to package level. Both cards incorporate edge
sensors.
MEMBRANE
REPLACEMNT CORE
10. MODEL
8 wafers of varying ‘quality were used in the model ranging from high yield to little or even no yield, ‘dead’ sites
were filtered out of the data. Sites were classified as dead if they exhibited no gain (due to mechanical faults). The
model was developed. A correlation coefficient of 90% was obtained with the model. The correlation coefficient of
the model is higher than any one PCM parameter to RF Device prediction, as shown in the following chart
.
Notice that the predicted gain (resulting from all of the variables) has a stringer correlation than any one of the
variables taken alone.
DEPLOYMENT
The model coefficients were inserted into an STM
language batch script and used to generate a summary PCM data
report. Data for that wafer in unloaded and run through the model algorithm. The output of the algorithm is the
predicted RF device gain for each PCM site using the 10 PCM parameters chosen for the model. A user calls the
program and enters a wafer number and the script generates a quality factor, this is the percentage of PCM sites that
have predicted gain that falls within the spec limits for the device, wafer RF gain contour map, PCM contour maps.
In addition the percentage of sites that pass PCM limits are also printed for each PCM spec limit both exclusively
and inclusively.
OUTPUT REPORT for each wafer with predicted device gain and yield.
Page 2 including a 3D plot of the predicted device paramter (highest pareto contributor to yield loss)
CORRELATION from Excel
vp1_50 vp10_50 subvth gm1_50 gm10_50 gm50_50 rch_50 nivdp gmextr_50 rout_50 gain predicted gain
vp1_50 100.00%
vp10_50 94.59% 100.00%
subvth 73.52% 47.53% 100.00%
gm1_50 47.99% 18.96% 90.48% 100.00%
gm10_50 73.74% 50.17% 95.12% 84.37% 100.00%
gm50_50 83.88% 71.92% 77.17% 54.30% 86.16% 100.00%
rch_50 58.06% 69.72% 11.76% -3.01% 7.87% 36.48% 100.00%
nivdp 0.92% 6.89% -11.90% -20.85% -28.99% -34.93% 11.59% 100.00%
gmextr_50 64.16% 49.20% 71.17% 57.67% 82.93% 84.41% 3.27% -44.09% 100.00%
rout_50 65.59% 50.70% 71.90% 57.76% 79.48% 76.80% 7.49% -30.60% 72.52% 100.00%
gain 74.22% 58.73% 78.53% 65.42% 84.34% 77.70% 10.98% -14.99% 78.75% 76.03% 100.00%
predicted gain 82.36% 65.17% 87.14% 72.60% 93.59% 86.22% 12.18% -16.64% 87.38% 84.37% 90.12% 100.00%
11. RESULTS:
The model has been used for wafers not included in the original model and has been working well. Some of the
parameters have been outside of the experience of the model. The model has performed well both in extrapolation
and interpolation. The accuracy however, can still can be improved by incorporating new data that is outside of the
past experience.
12. Here two models were created; one algebraic and one neural network and the results
compared.
Figure 1
Model: Predicted vs Actual Device Gain
0
5
10
15
20
25
30
35
40
1
64
127
190
253
316
379
442
505
568
631
694
757
820
883
946
1009
1072
1135
1198
1261
1324
1387
1450
1513
1576
1639
Data Line Number
GainindB
Neural Network
Excel Predict
Actual Gain
The graph in figure 1 plots the actual performance versus the predicted performance for both the
algebraic model created in excel and the neural network model. Data from subsequent runs was
used. This is not the modeled data. Note that the neural network data averaged out noise better
than the algebraic model.
Noise in dependant variable:
Several things have been learned subsequent to this which point out the need to understand the
data and the system. This device was found to be very sensitive to temperature. Temperature of
the wafer, even though on a controlled temperature chuck at 30 degrees C varied with room
temperature. Room temperature varied widely. This is due to the poor thermal conductivity of
GaAs. The top surface presents a large thermal face to the ambient room air, the back of the
wafer to the chuck. Thermal gradients therefore developed over the thickness of the wafer (12 mil)
causing a variation in temperature of the active area. The limiting amplifier gain is measured in the
open loop state (minimum input, no feedback, therefore maximum sensitivity to transistor
parameters) . A thermostream was later added to control the temperature of the air above the
wafer and of the probes, to match the chuck temperature. Long term traceability of wafer
standards kept a +/- .03 dB long term tracability to maximize wafer and package yield.
13. An additional error vector was found in a phenomenon similar to backgating, but not backgating.
Setting a negative voltage on the chuck would pull up gain (rather than decrease it as in
backgating) by as much as 4 or 5 dB. This usually occurred on devices which exhibited low gain to
start with (but not exclusively). I assume that the negative voltage was sweeping out trapped
charges in the channel of the FET’s, thus increasing the gain of the devices. This appeared to be
a random process defect that ranged from non-existent to severe and was dependant on location
of the device on the wafer. One could assume that PCM data would contain the same degradation
and still correlate to the device data. That would be incorrect in this case.
The PCM data was measured with the industry standard of +2.5 volt drain to source voltage.
However the device was biased with negative voltage. Thus the FET channel saw a negative
chuck (at 0 volts) with respect to the positive channel during PCM testing. In this configuration the
trapped charge carriers are swept out of the junction and the FET is enhanced. Thus the PCM
measurements were made with the test FET in an enhanced state.
The device is powered up with a negative voltage (chuck at 0 volts), the chuck appears positive to
the channel. In this state the FET does not have the trapped charges swept out of the channel and
the transistors are operating in a degraded state.
The effect of this is to add noise to the correlation. The important considerations are:
1) Averaging out the noise and assigning error bars to the prediction.
2) Ultimately, the goal is to eliminate the errors themselves.
A secondary form of modeling involves modeling the foundry. In this instance, a correlated PCM
parameter is modeled with respect to related parameters. The goal of this model is to see if the
process is behaving differently. While Control charts check for individual parameter centering, this
model would check for how related parameters vary with respect to each other. An example is Gm
10ma/mm predicted by Gm 20 ma/mm, Idss. The output would be represented on a bulls-eye plot
as deviation of the actual parameter form the predicted parameter (predicted from the related
parameters)
Summary:
Model can de developed to predict health of wafer and robustness error bars may be included to
include uncertainty in the prediction.
‘Human analysis is typically limited to looking at one independent variable versus one dependant
variable. O good analyst can visualize two independent variables interacting with one dependant
variable. Higher order visualizations are a problem however. Three independant variables
interacting with a fourth dependant variable requires a four dimensional plot. There have been
attempts at using color and even hypecube representations. Things get progressively worse
beyond 4 dimesional analysis. There are some difficulties with this in a three dimensional
existence.
A model however, has no such limitation and can deal with very high order dimensional anaylsis.
The accuracy of the model will be a function of:
1) The quality and type of the PCM measurements used.
3) The quality of the RF device probing.
4) The data selection criteria.
5) Understanding of the device, the PCM measurements and the modelling methodology.
14. Sensitivity analysis:
A further deployment after the model has been created is a sensitivity analysis that moves
parameters with respect to each other and measures the effect on the modeled output parameter.
Some modeling packages do include this feature, for those that do not, the effect can be scripted
or embedded in a GUI front end.
Additional monitoring:
Foundry can be monitored with control charts (check for design/process centering)
Foundry process can be checked for systemic variation by measuring deviation of a PCM
parameter from the predicted PCM parameter, to look for second order relationship changes in the
PCM data.
REFERENCES
Special thanks to Dave Harrison (AT&T GaAs Marketing Manager for Wide Area Network Circuits) for his help and advice in
preparing this paper. Jans Ransijn, AT&T GaAs designer of the LG1605 limiting amp. Jeff Williams at Cascade microtech for
characterizing the GaAs calibration standard I developed. Steve Smith at Cascade who worked with me to develop the high
speed membrane probe card. Gregg Bole at GGB Industries who worked with me to develop the high speed probe card for the
reduced pad frame version of the limiting amp. Eric King formerly with Neural Ware for collaboration on the concept of
deploying a neural network to model III/IV electrical performance.