AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Circuit Optimisation using Device Layout Motifs

Circuit designers face great challenges as CMOS devices continue to scale to nano dimensions, in particular, stochastic variability caused by the physical properties of transistors. Stochastic variability is an undesired and uncertain component caused by fundamental phenomena associated with device structure evolution, which cannot be avoided during the manufacturing process. In order to examine the problem of variability at atomic levels, the 'Motif' concept, defined as a set of repeating patterns of fundamental geometrical forms used as design units, is proposed to capture the presence of statistical variability and improve the device/circuit layout regularity. A set of 3D motifs with stochastic variability are investigated and performed by technology computer aided design simulations. The statistical motifs compact model is used to bridge between device technology and circuit design. The statistical variability information is transferred into motifs' compact model in order to facilitate variation-aware circuit designs. The uniform motif compact model extraction is performed by a novel two-step evolutionary algorithm. The proposed extraction method overcomes the drawbacks of conventional extraction methods of poor convergence without good initial conditions and the difficulty of simulating multi-objective optimisations. After uniform motif compact models are obtained, the statistical variability information is injected into these compact models to generate the final motif statistical variability model. The thesis also considers the influence of different choices of motif for each device on circuit performance and its statistical variability characteristics. A set of basic logic gates is constructed using different motif choices. Results show that circuit performance and variability mitigation can benefit from specific motif permutations. A multi-stage optimisation methodology is introduced, in which the processes of optimisation are divided into several stages. Benchmark circuits show the efficacy of the proposed methods. The results presented in this thesis indicate that the proposed methods are able to provide circuit performance improvements and are able to create circuits that are more robust against variability

Conference on Binary Optics: An Opportunity for Technical Exchange

The papers herein were presented at the Conference on Binary Optics held in Huntsville, AL, February 23-25, 1993. The papers were presented according to subject as follows: modeling and design, fabrication, and applications. Invited papers and tutorial viewgraphs presented on these subjects are included

Proceedings of the Second Caltech Conference on Very Large Scale Integration, held at the California Institute of Technology 19-21 January, 1981 ; Organized by the Caltech Computer Science Department and the Caltech Industrial Associates Office, and sponsored by Caltech Industrial Associates and the National Science Foundation

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Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

The Boston University Photonics Center annual report 2009-2010

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center for the period from July 2009 through June 2010. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This report summarizes activities of the Boston University Photonics Center (BUPC) during the period July 2009 through June 2010. These activities span the Center’s complementary missions in education, research, technology development, and commercialization. In education, twenty-three BUPC graduate students received Ph.D. diplomas. BUPC faculty taught thirty-one photonics courses. Five graduate students were funded through the Photonics Fellowship Program. BUPC supported a Research Experiences for Undergraduates (REU) site in Photonics, which hosted summer interns in a ten-week program. Each REU student presented their research results to a panel of faculty and graduate students. Professors Goldberg and Swan continued their work with K-12 student outreach programs. Professor Goldberg’s Boston Urban Fellows Project started its sixth year. Professor Swan’s collaborative Four Schools for Women in Engineering program entered its third year. For more on our education programs, turn to the Education section on page 67. In research, BUPC faculty published journal papers spanning the field of photonics. Twelve patents were awarded to faculty this year for new innovations in the field. A number of awards for outstanding achievement in education and research were presented to BUPC faculty members. These honors include NSF CAREER Awards for Professors Altug, Dal Negro and Reinhard. New external grant funding for the 2009-2010 fiscal year totaled $21.1M, including$4.0M through a Cooperative Agreement with the U.S. Army Research Laboratory (ARL). For more information on our research activities, turn to the Research section on page 24. In technology development, the Department of Defense (DoD) continued to support the COBRA prototype systems. These photonics-technologies were pioneered by BUPC faculty and staff and have been deployed for field test and use at the United States Army Medical Research Institute for Infectious Diseases. New technology development projects for nuclear weapon detection, biodosimetry and terahertz imaging were launched and previously developed technologies for bacterial and viral sensing advanced toward commercial transition. For more information on our technology development pipeline and projects, turn to the Technology Development section on page 54. In commercialization, the business incubator continues to operate at capacity. Its tenants include more than a dozen technology companies with core business interests primarily in photonics and life sciences. It houses several companies founded by current and former BU faculty and students and provides students with an opportunity to assist, observe, and learn from start-up companies. For more information about business incubator activities, turn to the Business Incubation chapter in the Facilities and Equipment section on page 84. In early 2010, the BUPC unveiled a five-year strategic plan as part of the University’s comprehensive review of centers and institutes. The BUPC strategic plan will enhance the Center’s position as an international leader in photonics research. For more information about the strategic plan, turn to the BUPC Strategic Plan section on page 8