IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Auromation science enginering

Elysium Technologies Private Limited
ISO 9001:2008 A leading Research and Development Division
Madurai | Chennai | Trichy | Coimbatore | Kollam| Singapore
Website: elysiumtechnologies.com, elysiumtechnologies.info
Email: info@elysiumtechnologies.com

IEEE Final Year Project List 2011-2012

Abstract AUTOMATION SCIENCE AND ENGINEERING 2011 - 2012

01 A Car-Seat Example of Automated Anthropomorphic Testing of Fabrics Using Force-Controlled Robot Motions

For the last years, automation is widely used to relieve humans from repetitive tasks, primarily and firstly within manufacturing. However,
for products with less ideal (or hard to model) properties, and when forces depends on human interaction, automated testing has not
been explored until now. This work presents the analysis of the (human-dependent) motions/ forces based on a fully implemented test
case for car-seat testing. For emulation of the corresponding mechanical wear, an experimental test bench was developed. A sensor mat
with a pressure gauge net was used in the test bench to determine the relevant loads, and the corresponding movements performed by
the humans when sitting in a car seat were acquired by means of a photogrammetry system. Finally, to automate the reproduction of
such movements by means of a dummy held by a robot, several controllers have been developed to regulate the force applied by the
dummy on the seat. Simplicity and force-control performance for the human replication was also investigated in this work, showing the
benefit of freely programmable (open) force control. The developed system has many practical applications, as allowing the analysis of

Madurai Trichy Kollam
Elysium Technologies Private Limited Elysium Technologies Private Limited Elysium Technologies Private Limited
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eMail: elysium.trichy@gmail.com
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the wearing caused by these movements on the seat upholstery. Thus, force controlled testing of fabrics using robots is a viable option.

02 A Framework to Model the Topological Structure of Supply Networks

Topological structure is considered more and more important in managing a supply network or predicting its development.
In this paper, a new framework is proposed to model the topological structure of supply networks, where different types of
supply networks can be created just by introducing different supplier-customer connecting rules. Generally, the networks
created in the framework are much different from the random networks with the same degree sequences. The revealed
phenomenon suggests that real-world supply networks may benefit from its intrinsic mechanism on flexibility, efficiency,
and robustness to target attacks.

03 A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks

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Our study concerns an important current problem, that of diffusion of information in social networks. This problem has
received significant attention from the Internet research community in the recent times, driven by many potential
applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem,
which involves discovering a small subset of influential players in a given social network, to perform a certain task of
information diffusion. The target set selection problem manifests in two forms: 1) top- nodes problem and 2) -coverage
problem. In the top- nodes problem, we are required to find a set of key nodes that would maximize the number of nodes
being influenced in the network. The -coverage problem is concerned with finding a set of key nodes having minimal size
that can influence a given percentage of the nodes in the entire network. We propose a new way of solving these problems
using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach
leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top- nodes
problem and the -coverage problem. We compare the performance of the proposed SPIN algorithms with well known
algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six
real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and
Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient.

04 A SNCCDBAGG-Based NN Ensemble Approach for Quality Prediction in Injection Molding Process

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This paper presents a SNCCDBAGG-based neural network (NN) ensemble approach for quality prediction in injection molding process.
Bagging is used to create NNs for the ensemble by independently training these NNs on different training sets. Negative correlation
learning via correlation-corrected data (NCCD) is used to achieve negative correlation of each network’s error against errors for the
rest of the ensemble by training transformed target data for NN in the ensemble as the desired network output for some epochs. A
selection-based strategy is proposed to improve generalization ability when combining Bagging and NCCD.

05 A Three Degree-of-Freedom Optical Orientation Measurement Method for Spherical Actuator Applications

The advance of robotics, actuators and manufacturing technology motivates the research on measurement of multiple
degree-of-freedom (DOF) rotational motions. A novel laser-based noncontact high-precision spherical displacement
measurement methodology has been proposed for spherical actuator applications in this paper. The laser detector is
utilized to measure the distance from the target to the detector on several light spots, and thus to calculate the rotation
angle of the rigid body in three directions. As there is no physical contact between the laser detector and the moving
body, additional mass/moment of inertia and friction on the rotor are avoided, and thus the working efficiency of moving
body can be improved. The algorithm of orientation angles has been derived. Experimental apparatuses have been
developed to evaluate the working performance of the measurement method. Comparison between experimental and
analytical results shows that the proposed method can achieve high-precision measurement for multi-DOF rotational

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motions. Precision of the laser-based angular displacement measurement method can be improved further by increasing
the slot density on the rigid body or using new models of laser detectors.

06 A Totally Decoupled Piezo-Driven XYZ Flexure Parallel Micropositioning Stage for Micro/Nanomanipulation

This paper reports the design and development processes of a totally decoupled flexure-based XYZ parallel-kinematics
micropositioning stage with piezoelectric actuation. The uniqueness of the proposed XYZ stage lies in that it possesses
both input and output decoupling properties with integrated displacement amplifiers. The input decoupling is realized by
actuation isolation using double compound parallelogram flexures with large transverse stiffness, and the output
decoupling is implemented by employing two-dimensional (2-D) compound parallelogram flexures. By simplifying each
flexure hinge as a two-degree-of-freedom (2-DOF) compliant joint, analytical models of kinematics, statics, and dynamics
of the XYZ stage are established and then validated with finite-element analysis (FEA). The derived models are further
adopted for optimal design of the stage through particle swarm optimization (PSO), and a prototype of XYZ stage is

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fabricated for performance tests. The nonsymmetric hysteresis behavior of the piezo-stage is identified with the modified
Prandtl-Ishlinskii (MPI) model, and a control scheme combining the inverse model-based feedforward with feedback
control is constructed to compensate the plant nonlinearity and uncertainty. Experimental results reveal that a submicron
accuracy 1-D and 3-D positioning can be achieved by the system, which confirms the effectiveness of the proposed
mechanism and controller design as well.

07 Adaptive Measurement for Automated Field Reconstruction and Calibration of Magnetic Systems

This paper presents an efficient method to adaptively determine the locations for taking measurements for automated
calibration of electromagnetic systems using reconstructed magnetic fields. This coupled measurement- computation
method solves the Laplace’s equation with measured boundary conditions. Along with the formulation of two selection
criteria (chord-height and data-spacing), an adaptive scanning algorithm has been developed, which bases four local
measurements to determine the next measurement point. This adaptive method, which relaxes the assumption of
approximately known structure, has been illustrated (with experimental verification) with three practical applications;
electromagnetic velocity probe, electromagnetic flow-meter (EMF) and spherical motor. Comparisons against published

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data demonstrate that the adaptive measurement algorithm greatly reduces the number of measurements and shortens
the scanning route length of all three applications without sacrificing the accuracy of the computed results. Dry
calibration results of an EMF were also experimentally verified against test data obtained from a standard flow-rig
confirming that a relative error of 0.2% can be achieved. This finding makes the cost-effective dry calibration a practical
alternative to the conventional flow rig calibration. As demonstrated on a spherical motor, the flexibility to include a least-
square curve fit offers a practical means to filter measurement noise.

08 An Enhanced Nested Partitions Algorithm Using Solution Value Prediction

This paper presents an efficient method to adaptively determine the locations for taking measurements for automated
calibration of electromagnetic systems using reconstructed magnetic fields. This coupled measurement- computation
method solves the Laplace’s equation with measured boundary conditions. Along with the formulation of two selection
criteria (chord-height and data-spacing), an adaptive scanning algorithm has been developed, which bases four local

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measurements to determine the next measurement point. This adaptive method, which relaxes the assumption of
approximately known structure, has been illustrated (with experimental verification) with three practical applications;
electromagnetic velocity probe, electromagnetic flow-meter (EMF) and spherical motor. Comparisons against published
data demonstrate that the adaptive measurement algorithm greatly reduces the number of measurements and shortens
the scanning route length of all three applications without sacrificing the accuracy of the computed results. Dry
calibration results of an EMF were also experimentally verified against test data obtained from a standard flow-rig
confirming that a relative error of 0.2% can be achieved. This finding makes the cost-effective dry calibration a practical
alternative to the conventional flow rig calibration. As demonstrated on a spherical motor, the flexibility to include a least-
square curve fit offers a practical means to filter measurement noise.

09 An Integer Programming Approach for Analyzing the Measurement Redundancy in Structured Linear Systems

A linear system whose model matrix is of size is considered structured if some row vectors in the model matrix are
linearly dependent. Computing the degree of redundancy for structured linear systems is proven NP-hard. Previous

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computation strategy is divide-and-conquer, materialized in a bound-and-decompose algorithm, which, when the required
conditions are satisfied, can compute the degree of redundancy on a set of much smaller submatrices instead of directly
on the original model matrix. The limitation of this algorithm is that the current decomposition conditions are still
restrictive and not always satisfied for many applications. We present a mixed integer programming (MIP) formulation of
the redundancy degree problem and solve it using an existing MIP solver. Our numerical studies indicate that our
approach outperforms the existing methods for many applications, especially when the decomposition conditions are not
satisfied. The main contribution of the paper is that we tackle this challenging problem from a different angle and test a
promising new approach. The resulting approach points to a path that can potentially solve the problem in its entirety.

10 Approximating the Performance of a Batch Service Queue Using

Batching plays an important role in semiconductor fabs, and it can lead to inefficiency if not treated with care. The
performance of parallel batch processes is often approximated by the G/G/1–based approximate models. By carefully
examining the existing models, the dependence between queueing time and wait-tobatch time has been identified. A new
improved model for parallel batch systems is proposed to exploit this dependence. The computation of the new model is
still simple and fast, but it gives better approximation by reducing the systematic error in earlier models which ignored the
dependence between queueing time and wait-tobatch time.

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11 Artificial Cognition in Production Systems

Today’s manufacturing and assembly systems have to be flexible to adapt quickly to an increasing number and variety of
products, and changing market volumes. To manage these dynamics, several production concepts (e.g., flexible,
reconfigurable, changeable or autonomous manufacturing and assembly systems) were proposed and partly realized in
the past years. This paper presents the general principles of autonomy and the proposed concepts, methods and
technologies to realize cognitive planning, cognitive control and cognitive operation of production systems. Starting with
an introduction on the historical context of different paradigms of production (e.g., evolution of production and planning
systems), different approaches for the design, planning, and operation of production systems are lined out and future
trends towards fully autonomous components of an production system as well as autonomous parts and products are
discussed. In flexible production systems with manual and automatic assembly tasks, human-robot cooperation is an
opportunity for an ergonomic and economic manufacturing system especially for low lot sizes. The state-of-the-art and a
cognitive approach in this area are outlined. Furthermore, introducing self-optimizing and self-learning control systems is
a crucial factor for cognitive systems. This principles are demonstrated by a quality assurance and process control in
laser welding that is used to perform improved quality monitoring. Finally, as the integration of human workers into the
workflow of a production system is of the highest priority for an efficient production, worker guidance systems for manual

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assembly with environmentally- and situationally dependent triggered paths on state-based graphs are described in this
paper.

12 Automated Multi-Agent Search Using Centroidal Voronoi Configuration

This paper addresses the problem of automated multiagent search in an unknown environment.Autonomous agents
equipped with sensors carry out a search operation in a search space, where the uncertainty, or lack of information about
the environment, is known a priori as an uncertainty density distribution function. The agents are deployed in the search
space to maximize single step search effectiveness. The centroidal Voronoi configuration, which achieves a locally
optimal deployment, forms the basis for the proposed sequential deploy and search strategy. It is shown that with the
proposed control law the agent trajectories converge in a globally asymptotic manner to the centroidal Voronoi
configuration. Simulation experiments are provided to validate the strategy.

13 Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning

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The paper presents an approach helping developers to maintain source code identifiers and comments consistent with
high-level artifacts. Specifically, the approach computes and shows the textual similarity between source code and related
high-level artifacts. Our conjecture is that developers are induced to improve the source code lexicon, i.e., terms used in
identifiers or comments, if the software development environment provides information about the textual similarity
between the source code under development and the related high-level artifacts. The proposed approach also
recommends candidate identifiers built from high-level artifacts related to the source code under development and has
been implemented as an Eclipse plug-in, called COde Comprehension Nurturant Using Traceability (COCONUT). The paper
also reports on two controlled experiments performed with master’s and bachelor’s students. The goal of the experiments
is to evaluate the quality of identifiers and comments (in terms of their consistency with high-level artifacts) in the source
code produced when using or not using COCONUT.

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14 Bouc–Wen Modeling and Inverse Multiplicative Structure to Compensate Hysteresis Nonlinearity in Piezoelectric

A new approach to compensate the strong hysteresis nonlinearity in piezoelectric materials is proposed. Based on the
inverse multiplicative scheme, the approach avoids models inversion as employed in existing works. The compensator is
therefore simple to implement and does not require additional computation as soon as the direct model is available. The
proposed compensation technique is valuable for hysteresis that are modeled with the Bouc–Wen set of equations. Note to
Practitioners—Since many years, the hysteresis nonlinearity in piezoelectric micro/nanoactuators (AFM, microgrippers,
etc.) has been moving many researchers. Despite the high resolution and the high speed of these materials, the hysteresis
strongly compromises the accuracy. If feedback control laws could easily improve the performances, their use in
micro/nanoactuators and small systems are constrained by the luck of convenient sensors. On the one hand, accurate and
high bandwidth sensors are very expensive and bulky (accelerometers, optical sensors). On the other hand, integrable
sensors are highly sensitive to noises and are very fragile (strain gage). To surpass the use of sensors, feedforward control
techniques have been used. These techniques represent a great interest in an integration/package point-of-view but the
existing methods show a complexity in terms of computation and implementation.

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15 Decision Fusion from Heterogeneous Sensors in Surveillance Sensor Systems

Using multiple, heterogeneous sensors in surveillance systems is desirable, not only to tolerate sensor failures, but also to
increase the accuracy of the event detection process and to provide complementary capability under different operating
conditions. In the operation of multiple, heterogeneous sensors, we may encounter inconsistent sensor observations.
Motivated by the need to make coherent decisions, we propose in this study a decision scheme to determine the right
interpretations of sensor outputs when conflict arises. The proposed decision rule considers sensor heterogeneity in a
surveillance system, while attempting to minimize the expected misclassification cost. Case studies of the surveillance
sensor system in a major U.S. port demonstrate that the proposed decision scheme achieves a better robustness in the
presence of sensor failures than the popular -out-of- decision fusion rule.

16 Distributed Optimization for Model Predictive Control of Linear Dynamic Networks with Control-Input and Output Constraints

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A linear dynamic network is a system of subsystems that approximates the dynamic model of large, geographically
distributed systems such as the power grid and traffic networks.A favorite technique to operate such networks is
distributed model predictive control (DMPC), which advocates the distribution of decision-making while handling
constraints in a systematic way. This paper contributes to the state-of-the-art of DMPC of linear dynamic networks in two
ways. First, it extends a baseline model by introducing constraints on the output of the subsystems and by letting
subsystem dynamics to depend on the state besides the control signals of the subsystems in the neighborhood. With these
extensions, constraints on queue lengths and delayed dynamic effects can be modeled in traffic networks. Second, this
paper develops a distributed interior-point algorithm for solving DMPC optimization problems with a network of agents, one
for each subsystem, which is shown to converge to an optimal solution. In a traffic network, this distributed algorithm
permits the subsystem of an intersection to be reconfigured by only coordinating with the subsystems in its vicinity.

17 Excursion Yield Loss and Cycle Time Reduction in Semiconductor Manufacturing

The importance of cycle time reduction is well known to the semiconductor manufacturing industry in the sense of reduced
inventory costs and faster response to the market. Less emphasized is the fact that the overall die yield is also closely
related to cycle time. In particular, some yield losses are due to “excursions,” when process or equipment shift out of
specifications. While some and perhaps most excursions are detected by in-line inspections, some are not detected until

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the wafers are tested in the probing area after fabrication. A long production cycle time will expose significant amounts of
wafers in production to defective processing by such excursions. This paper introduces analytical formulas to quantify the
revenue losses due to excursions not detected until end-of-line testing as a function of manufacturing cycle time, excursion
probabilities and kill rates. The formulas provide a means to evaluate the revenue gains due to cycle time reduction, based
on the assumption that the average selling prices of semiconductor products are declining steadily at predictable rates.

.
18 KnitSketch: A Sketch Pad for Conceptual Design of 2D Garment Patterns

In this paper, we present a new sketch-based system — KnitSketch, to improve the efficiency of process planning for
knitting garments at an early design stage. The KnitSketch system utilizes sketching interface with the pen-paper metaphor
and users only need to draw outlines of different parts of the garment. Based on sketching understanding, the system
automatically makes reasonable geometric inferences about the process-planning data of the garment. The system is
designed for nonprofessional users and can design diverse garment styles by freehand drawings. The contributions of this
work include contextual extraction of reusable data from sketches, a MDG structure for sketch beautification, and an
integrated system with natural expression and effective communication that reduces the cognitive load of human beings.
User experience shows that the proposed system helps designers focus on the task instead of the designing tools, and
thus improves the efficiency and productivity of human beings.

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19 Manufacturing Intelligence to Exploit the Value of Production and Tool Data to Reduce Cycle Time

Cycle time reduction is crucial for semiconductor wafer fabrication companies to maintain competitive advantages as the
semiconductor industry is becoming more dynamic and changing faster. According to Little’s Law, while maintaining the
same throughput level, the reduction in Work-in-Process (WIP) will result in cycle time reduction. On one hand, the existing
queueing models for predicting the WIP of tool sets in wafer fabrication facilities (fab) have limitations in real settings. On
the other hand, little research has been done to predict the WIP of tool sets with tool dedication and waiting time constraint
so as to control the corresponding WIP levels of various tool sets to reduce cycle time without affecting throughput. This
study aims to fill the gap by proposing a manufacturing intelligence (MI) approach based on neural networks (NNs) to exploit
the value of the wealthy production data and tool data for predicting the WIP levels of the tool sets for cycle time reduction.
To validate this approach, empirical data were collected and analyzed in a leading semiconductor company. The comparison
results have shown practical viability of this approach. Furthermore, the proposed approach can identify and improve the
critical input factors for reducing the WIP to reduce cycle time in a fab.

20 Module Selection and Supply Chain Optimization for Customized Product Families Using Redundancy and Standardization

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In this paper, we study the problem of configuring a product family which has to satisfy diversified customer requirements.
Modular design strategies allow a bill of materials to be generated for various finished products from a limited subset of
modules. Simultaneously, a production location must be selected for the manufacture of each module. From a product point-
of-view, the strategies adopted are often extreme, proposing either the manufacturing of the total diversity (all possible
products) or a limited set of standardized products (only a few products are proposed). The objective of this paper is to
investigate intermediate cases on this continuum, in order to better understand the potential for profit. Our comparison is
based on the product family configuration (selection of the best modules) that minimizes production and transportation
costs under time constraints.

21 Multiclass Flow Line Models of Semiconductor Manufacturing Equipment for Fab-Level Simulation

For multiclass flow line models, we identify a class of service times that allow a decomposition of the system into subsets
of servers called channels. In each channel, the customer delay is well structured and we develop a recursion to calculate
it. The recursions provide an alternative to the elementary evolution equations. By considering batch arrivals and
restricting the structure of the model, the recursions can require nearly one order of magnitude less computation than is
otherwise possible. Flow lines can be used as models for semiconductor manufacturing equipment such as multicluster or
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clustered photolithography tools. The models allow for internal wafer buffers and setups that are wafer location dependent.
The models have shown to be very accurate in tests with data from clustered photolithography tools in production. As
such, the models may serve as good candidates to improve the fidelity of existing equipment models in fablevel simulation.

22 Multiresource Shop Scheduling With Resource Flexibility and Blocking

This paper proposes a general scheduling model that extends job-shop scheduling models to incorporate important features
of real manufacturing systems. More precisely, each operation can be performed in different modes and requires a different
set of resources depending on the mode. Further, we consider blocking constraints that requires to hold resources used for
an operation till resources needed for the next operation of the same job are available. A shortest path approach extending
the classical geometric approach is proposed for the two-job case. A greedy heuristic is then proposed to schedule jobs by
considering jobs sequentially, grouping scheduled jobs into a combined job and then scheduling it and the next
unscheduled job using the shortest path approach. A metaheuristic is then used to identify effective job sequences.
Extensive numerical experimentation proves the efficiency of our approach.

23 Optimal Paint Gun Orientation in Spray Paint Applications—Experimental Results

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In this paper, we present the experimental results of a new spray paint algorithm presented in previous publications. Both
theory and simulations indicate that the proposed method allows a robotic manipulator to paint a given surface using
substantially lower joint torques than with conventional approaches. In this paper, we confirm this by implementing the
algorithm on an ABB robot and we find that the joint torques needed to follow the trajectory are substantially lower than for
the conventional approach. The approach presented is based on the observation that a small error in the orientation of the
end effector does not affect the quality of the paint job. It is far more important to maintain constant velocity for the entire
trajectory. We thus propose to allow a small error in the specification of the end-effector orientation, and we show how this
allows us to obtain a higher constant speed throughout the trajectory. In addition, to improve the uniformity of the paint
coating we are also able perform the paint job in less time.

24 Optimal Scheduling of Biochemical Analyses on Digital Microfluidic Systems

Digital microfluidic systems (DMFS) are an emerging class of lab-on-a-chip systems that manipulate individual droplets of
chemicals on a planar array of electrodes. The biochemical analyses are performed by repeatedly moving, mixing, and

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splitting droplets on the electrodes. Mixers and storage units, composed of electrodes, are two important functional
resources. Mixers perform droplet mixing and splitting operations, while storage units store droplets that have been
produced for subsequent mixings. In this paper, we focus on minimizing the completion time of biochemical analyses by
exploiting the binary tree representation of analyses to schedule mixing operations. Using pipelining, we overlap mixing
operations with input and transportation operations. We find the lower bound of the mixing completion time based on the
tree structure of input analyses, and calculate the minimum number of mixers required to achieve the lower bound. We
present a scheduling algorithm for the case with a specified number of mixers , and prove it is optimal to minimize the
mixing completion time. We also analyze resource constraint issues for two extreme cases. For the case with just one
mixer, we prove that all schedules that keep the mixer busy at all times result in the same mixing completion time and then
design algorithms for scheduling and to minimize the number of storage units. For the case with zero storage units, we find
the minimum number of mixers required. We extend our analyses and algorithms assuming identical mixing durations to
the case of different mixing durations. Finally, we illustrate the benefits of our scheduling methods on an example of DNA
polymerase chain reaction (PCR) analysis.

25 Optimal Scheduling of Multicluster Tools With Constant Robot Moving Times, Part II: Tree-Like Topology Configurations

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In this paper, we analyze optimal scheduling of a tree-like multicluster tool with single-blade robots and constant robot
moving times. We present a recursive minimal cycle time algorithm to reveal a multi-unit resource cycle for multicluster
tools under a given robot schedule. For a serial-cluster tool, we provide a closed-form formulation for the minimal cycle
time. The formulation explicitly provides the interaction relationship among clusters. We further present decomposition
conditions under which the optimal scheduling of multicluster becomes much easier and straightforward. Optimality
conditions for the widely used robot pull schedule are also provided. An example from industry production is used to
illustrate the analytical results. The decomposition and optimality conditions for the robot pull schedule are also illustrated
by Monte Carlo simulation for the industrial example.

26 Optimal Scheduling of Multicluster Tools With Constant Robot Moving Times, Part I: Two-Cluster Analysis

In semiconductor manufacturing, finding an efficient way for scheduling a multicluster tool is critical for productivity
improvement and cost reduction. This two-part paper analyzes optimal scheduling of multicluster tools equipped with
single-blade robots and constant robot moving times. In this first part of the paper, a resource-based method is proposed to
analytically derive closed-form expressions for the minimal cycle time of two-cluster tools. We prove that the optimal robot
scheduling of two-cluster tools can be solved in polynomial time. We also provide an algorithm to find the optimal schedule.

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Examples are presented to illustrate the proposed approaches and formulations.

27 Petri Net-Based Scheduling of Single-Arm Cluster Tools with Reentrant Atomic Layer Deposition Processes

For some wafer fabrication processes in cluster tools, e.g., atomic layer deposition (ALD), wafer revisiting is required.
Typically, in such processes, wafers need to visit two consecutive processing steps several times. Such a revisiting process
can be denoted as ___, where means the th-step and and _ mean the corresponding quantity of the processing
modules in and _ __th steps, and the number of visiting times. This paper conducts a study for scheduling single-arm
cluster tools with such a wafer revisiting process. The system is modeled by Petri nets (PNs) to guarantee the feasibility of
robot activities. Based on the model, a deadlock avoidance policy is presented. With the control policy, cycle time analysis
for the revisiting process is made. With the fact that wafer processing times are much longer than robot movement times in
cluster tools, it is shown that, when _ _ _ _, i.e., each step has only one processing module, the optimal one-wafer cyclic
schedule is deterministic and unique, and the minimal cycle time can be calculated by an analytical expression. It is also
shown that, when _ _and _ _ _or _ _ and _ _ _, the optimal one-wafer cyclic schedule can be obtained by finding
deterministic schedules and the one with the least cycle time. A novel analytical method is finally presented to schedule the
overall system containing such reentrant wafer flow. This represents a significant advance in single-arm cluster equipment

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automation. We study the problem of configuring a product family which has to satisfy diversified customer requirements.
Modular design strategies allow a bill of materials to be generated for various finished products from a limited subset of
modules. Simultaneously, a production location must be selected for the manufacture of each module. From a product point-
of-view, the strategies adopted are often extreme, proposing either the manufacturing of the total diversity (all possible
products) or a limited set of standardized products (only a few products are proposed). The objective of this paper is to
investigate intermediate cases on this continuum, in order to better understand the potential for profit. Our comparison is
based on the product family configuration (selection of the best modules) that minimizes production and transportation
costs under time constraints.

28 Productivity Improvement From Using Machine Buffers in Dual-Gripper Cluster Tools

Cluster tools (also referred to as robotic cells) are extensively used in semiconductor wafer fabrication. We consider the
problem of scheduling operations in an -machine cluster tool that produces identical parts (wafers). Each machine is
equipped with a unit-capacity input buffer and a unit-capacity output buffer. The machines and buffers are served by a dual-
gripper robot. Each wafer is processed on each of the machines, and the objective is to find a cyclic sequence of robot
moves that minimizes the long-run average time to produce a part or, equivalently, maximizes the throughput. We first obtain
a tight upper bound on the optimal throughput and then use this bound to obtain an asymptotically optimal sequence under
conditions that are common in practice. Next, we quantify the improvement in productivity that can be realized from the use

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of unit-capacity input and output buffers at the machines. Finally, we illustrate our analysis on cluster tools with realistic
parameters, based on our work with a Dallas-based semiconductor equipment manufacturer.

29 Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping

Color grading is a crucial step in the processing of fruits and vegetables that directly affects profitability, because the
quality of agricultural products is often associated with their color. Most existing automatic color grading systems determine
color quality either by directly comparing product color against a predefined and fixed set of reference colors or by using a
set of color separating parameters, often in three-dimensional color spaces. Using these methods, it is not convenient for
the user to adjust color preferences or grading parameters. In this paper, we present an effective and user-friendly color
mapping concept for automated color grading that is well suited for commercial production. User friendliness is often
viewed by the industry as a very important factor to the acceptance and success of automation equipment. This color
mapping method uses preselected colors of interest specific to a given application to calculate a unique set of coefficients
for color space conversion. The three-dimensional RGB color space is converted into a small set of color indices unique to
the application. In contrast with more complex color grading techniques, the proposed method makes it easy for a human
operator to specify and adjust color-preference settings Tomato and date maturity evaluation and date surface defect
detection are used to demonstrate the performance of this novel color mapping concept.

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30 Schedulability Analysis of Short-Term Scheduling for Crude Oil Operations in Refinery with Oil Residency

For short-term scheduling of crude oil operations in refinery, often crude oil residency time constraint and chargingtank-
switch-overlap constraint are ignored by mathematical programming models to make the problem solvable. Thus, a schedule
obtained by such mathematical programming models is infeasible and cannot be deployed. To solve this problem, this work
studies the short-term scheduling problem of crude oil operations in a control theory perspective. The system is modeled by
a hybrid Petri net and a short-term schedule is seen as a series of control commands. With this model, schedulability
analysis is carried out and schedulability conditions are presented. These conditions can be used as constraints for finding
a realizable and optimal refining schedule. Moreover, based on the proposed approach, a detailed schedule can be easily
obtained given a realizable refining schedule. In this way, the complexity for the short-term scheduling problem of crude oil
operations in refinery is greatly reduced and effective techniques and tools for practical applications can be obtained.

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31 Selection Schemes of Dual Virtual-Metrology Outputs for Enhancing Prediction Accuracy

Selection schemes between neural-network (NN) and multiple-regression (MR) outputs of a virtual metrology system (VMS)
are studied in this paper. Both NN and MR are applicable algorithms for implementing virtual-metrology (VM) conjecture
models. A MR algorithm may achieve better accuracy only with a stable process, whereas a NN algorithm may have superior
accuracy when equipment property drift or shift occurs. To take advantage of both MR and NN algorithms, the simple-
selection scheme (SS-scheme) is first proposed to enhance the VM conjecture accuracy. This SS-scheme simply selects
either NN or MR output according to the smaller Mahalanobis distance between the input process data set and the NN/MR-
group historical process data sets. Furthermore, a weighted-selection scheme (WS-scheme), which computes the VM output
with a weighted sum of NN and MR results, is also developed. This WS-scheme generates a well-behaved system with
continuity between the NN and MR outputs. Both the CVD and photo processes of a fifth-generation TFT-LCD factory are
adopted in this paper to test and compare the conjecture accuracy among the solo-NN, solo-MR, SS-scheme, and WS-
scheme algorithms. One-hidden-layered back-propagation neural network (BPNN-I) is applied to establish the NN conjecture
model. Test results show that the conjecture accuracy of the WS-scheme is the best among those solo-NN, solo-MR, SS-
scheme, and WS-scheme algorithms.

27



32 Spatial Variance Spectrum Analysis and Its Application to Unsupervised Detection of Systematic Wafer Spatial Variations

Investigation of wafer spatial variations is critical for semiconductor process/equipment optimization and circuit design.
The objective of spatial variation study is to differentiate the systematic variation component from the random component.
This is usually done by contrasting with a set of known systematic patterns based on engineering knowledge. However,
there could exist unknown systematic components remaining in the unexplained residuals and overlooked by the
conventional spatial variation study. In this paper, we develop a novel spatial variance spectrum _ to analyze the
systematic variations without any priori information of the systematic patterns. The is a series of spatial variations over a
range of spatial moving window sizes from the smallest spatial moving window consisting of only two metrology sites to
the largest one covering all metrology sites of the entire wafer. The can be used to characterize the wafer spatial variations
and to detect existence of systematic variations by a proposed hypothesis test. We also propose an index to summarize
from the the systematic proportion of the spatial variation. The proposed test and index of systematic variations will be
demonstrated and validated through both hypothetical examples and actual cases of wafer critical dimension (CD)
metrology data.

28



33 Statistical Weight Kinetics Modeling and Estimation for Silica Nanowire Growth Catalyzed by Pd Thin Film

This work intends to understand and model the kinetic aspect or the change of substrate weight over time in the selective
growth of silica nanowires (NWs) catalyzed through Pd thin film. Various adsorption-induced, diffusion-induced, or unified
vapor-liquid-solid (VLS) growth models have been developed to describe the NW length varying with time. Since NW
length has been difficult to be measured, substrate weight change is therefore used as an alternative in this study to
investigate growth kinetics of NWs.We investigate six different weight kinetics models in predicting weight changes during
growth. Model estimation and comparison are conducted using both maximum-likelihood estimation (MLE) and Bayesian
approaches. Owing to the embedded kinetics information in the nonlinear growth models, the Bayesian hierarchical model
is shown to be more desirable when process data is limited.

34 The Base Stock/Base Backlog Control Policy for a Make-to-Stock System With Impatient Customers

29



We study a single-stage production system that produces one product type. The system employs a base stock policy to
maintain an inventory of finished items and cope with random demand. During stockout periods, the system incurs three
types of potential customer loss: (a) balking, i.e., arriving customers may be unwilling to place orders and leave immediately;
(b) rejection, i.e., the system rejects new customer orders if its backlog has reached a certain limit, called the base backlog;
(c) reneging, i.e., outstanding customers waiting in queue may become impatient and withdraw their orders. The objective is
to determine the base stock and base backlog that maximize the mean profit rate of the system. This quantity is estimated
analytically using a finite capacity M/M/1 queueing model, in which the arrival rate is a decreasing but otherwise arbitrary
function of the backlog and customer reneging times have an arbitrary but known distribution. Certain properties are
established which ensure that the optimal control parameters can be determined in finite time by exhaustive search. The
model is then extended to take into account a fixed order quantity policy for replenishing raw material. Numerical results
show that managing inventories

35 WireWarping++: Robust and Flexible Surface Flattening With Length Control

30



Surface flattening has numerous applications in sheet manufacturing industries, such as garment industry, shoe industry,
toy industry, furniture industry, and ship industry. Motivated by the requirements of those industries, WireWarping approach
presented by Wang, 2008 is exploited to generate 2-D patterns with invariant length of feature and boundary curves.
However, strict length constraints on all feature curves sometimes cause large distortions on 2-D patterns, especially for
those 3-D surfaces which are highly non-developable. In this paper, we present a flexible and robust extension
ofWireWarping by introducing a new type of feature curves named elastic feature, which brings flexibility to shape control of
the resultant 2-D patterns. On these new feature curves, instead of strictly preserving the exact lengths, only the ranges of
their lengths are controlled. To achieve this function, a multiloop shape control optimization framework is proposed to find
the optimized 2-D shape among all possible flattening results with different length variations on those elastic feature curves,
while the lengths of other feature curves are kept unchanged. Besides, we also present a topology processing algorithm on
the network of feature curves to eliminate cases that lead to numerical singularity. Experimental results show that the
WireWarping++ can successfully flatten surface patches into 2-D patterns with more flexible shape control and more robust
numerical performance.

31

IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Auromation science enginering

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