SPICE MODEL of S5688G (Professional Model) in SPICE PARK

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SPICE MODEL of S5688G (Professional Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.

Technology

Device Modeling Report

COMPONENTS:
DIODE/ GENERAL PURPOSE RECTIFIER/ PROFESSIONAL
PART NUMBER: S5688G
MANUFACTURER: TOSHIBA

Bee Technologies Inc.

All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

PSpice model
Model description
parameter
IS Saturation Current
N Emission Coefficient
RS Series Resistance
IKF High-injection Knee Current
CJO Zero-bias Junction Capacitance
M Junction Grading Coefficient
VJ Junction Potential
ISR Recombination Current Saturation Value
BV Reverse Breakdown Voltage(a positive value)
IBV Reverse Breakdown Current(a positive value)
TT Transit Time
EG Energy-band Gap

All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

Forward Current Characteristic

Circuit Simulation Result

Evaluation Circuit

R1

0.01m

V1
0Vdc

U1 S5688G

0

All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

Comparison Graph

Circuit Simulation Result

Simulation Result

Vfwd(V) Vfwd(V)
Ifwd(A) %Error
Measurement Simulation
0.01 0.654 0.654 -0.061
0.02 0.688 0.686 0.247
0.05 0.726 0.729 -0.399
0.1 0.760 0.760 -0.053
0.2 0.794 0.793 0.151
0.5 0.838 0.837 0.107
1 0.880 0.875 0.557
2 0.912 0.918 -0.669
5 0.996 0.995 0.151

All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

Junction Capacitance Characteristic

Circuit Simulation Result

Evaluation Circuit

V2

0Vdc

V2 = 400 V1
V1 = 0
TD = 0
TR = 1u
TF = 10ns U1 S5688G
PW = 20us
PER = 50us

0

All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

Comparison Graph

Circuit Simulation Result

Simulation Result

Cj(pF) Cj(pF)
Vrev(V) %Error
Measurement Simulation
0 29.665 29.665 0.000
0.1 28.603 28.420 0.640
0.2 26.935 26.660 1.021
0.5 22.235 22.716 -2.163
1 19.051 19.561 -2.677
2 15.747 15.855 -0.686
5 11.817 11.786 0.262
10 9.250 9.100 1.626
20 7.129 7.039 1.257
50 4.932 4.995 -1.283
100 3.780 3.839 -1.566

All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

Reverse Recovery Characteristic

Circuit Simulation Result

Evaluation Circuit

R1

50
U1
V2 = 10.8V V1
V1 = -9.3V
TD = 0
TR = 10ns
TF = 10ns
PW = 20us
PER = 50us S5688G

0

Compare Measurement vs. Simulation

Measurement Simulation %Error
trj 860 ns 859 ns 0.116
trb 850 ns 852 ns 0.235

All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

Reverse Recovery Characteristic Reference

Measurement

Trj =860(ns)
Trb=580(ns)
Conditions: Ifwd=Irev=0.2(A), Rl=50

Example

Relation between trj and trb

All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

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Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they? Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality. You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.

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“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...

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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/ Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit. The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers. Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.

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SPICE MODEL of S5688G (Professional Model) in SPICE PARK

1. Device Modeling Report COMPONENTS: DIODE/ GENERAL PURPOSE RECTIFIER/ PROFESSIONAL PART NUMBER: S5688G MANUFACTURER: TOSHIBA Bee Technologies Inc. All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

2. PSpice model Model description parameter IS Saturation Current N Emission Coefficient RS Series Resistance IKF High-injection Knee Current CJO Zero-bias Junction Capacitance M Junction Grading Coefficient VJ Junction Potential ISR Recombination Current Saturation Value BV Reverse Breakdown Voltage(a positive value) IBV Reverse Breakdown Current(a positive value) TT Transit Time EG Energy-band Gap All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

4. Comparison Graph Circuit Simulation Result Simulation Result Vfwd(V) Vfwd(V) Ifwd(A) %Error Measurement Simulation 0.01 0.654 0.654 -0.061 0.02 0.688 0.686 0.247 0.05 0.726 0.729 -0.399 0.1 0.760 0.760 -0.053 0.2 0.794 0.793 0.151 0.5 0.838 0.837 0.107 1 0.880 0.875 0.557 2 0.912 0.918 -0.669 5 0.996 0.995 0.151 All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

5. Junction Capacitance Characteristic Circuit Simulation Result Evaluation Circuit V2 0Vdc V2 = 400 V1 V1 = 0 TD = 0 TR = 1u TF = 10ns U1 S5688G PW = 20us PER = 50us 0 All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

6. Comparison Graph Circuit Simulation Result Simulation Result Cj(pF) Cj(pF) Vrev(V) %Error Measurement Simulation 0 29.665 29.665 0.000 0.1 28.603 28.420 0.640 0.2 26.935 26.660 1.021 0.5 22.235 22.716 -2.163 1 19.051 19.561 -2.677 2 15.747 15.855 -0.686 5 11.817 11.786 0.262 10 9.250 9.100 1.626 20 7.129 7.039 1.257 50 4.932 4.995 -1.283 100 3.780 3.839 -1.566 All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

7. Reverse Recovery Characteristic Circuit Simulation Result Evaluation Circuit R1 50 U1 V2 = 10.8V V1 V1 = -9.3V TD = 0 TR = 10ns TF = 10ns PW = 20us PER = 50us S5688G 0 Compare Measurement vs. Simulation Measurement Simulation %Error trj 860 ns 859 ns 0.116 trb 850 ns 852 ns 0.235 All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

8. Reverse Recovery Characteristic Reference Measurement Trj =860(ns) Trb=580(ns) Conditions: Ifwd=Irev=0.2(A), Rl=50 Example Relation between trj and trb All Rights Reserved Copyright (C) Bee Technologies Inc. 2004

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