REDUCING POWER DURING MANUFACTURING TEST USING DIFFERENT ARCHITECTURES

Power during manufacturing test can be several times higher than power consumption in functional mode. Excessive power during test can cause IR drop, over-heating, and early aging of the chips. In this dissertation, three different architectures have been introduced to reduce test power in general cases as well as in certain scenarios, including field test. In the first architecture, scan chains are divided into several segments. Every segment needs a control bit to enable capture in a segment when new faults are detectable on that segment for that pattern. Otherwise, the segment should be disabled to reduce capture power. We group the control bits together into one or more control chains. To address the extra pin(s) required to shift data into the control chain(s) and significant post processing in the first architecture, we explored a second architecture. The second architecture stitches the control bits into the chains they control as EECBs (embedded enable capture bits) in between the segments. This allows an ATPG software tool to automatically generate the appropriate EECB values for each pattern to maintain the fault coverage. This also works in the presence of an on-chip decompressor. The last architecture focuses primarily on the self-test of a device in a 3D stacked IC when an existing FPGA in the stack can be programmed as a tester. We show that the energy expended during test is significantly less than would be required using low power patterns fed by an on-chip decompressor for the same very short scan chains

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Advanced Testing and Characterization of Bituminous Materials, Two Volume Set

Bituminous materials are used to build durable roads that sustain diverse environmental conditions. However, due to their complexity and a global shortage of these materials, their design and technical development present several challenges. Advanced Testing and Characterisation of Bituminous Materials focuses on fundamental and performance testin

Advanced Testing and Characterization of Bituminous Materials, Two Volume Set

Bituminous materials are used to build durable roads that sustain diverse environmental conditions. However, due to their complexity and a global shortage of these materials, their design and technical development present several challenges. Advanced Testing and Characterisation of Bituminous Materials focuses on fundamental and performance testin

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions