IEEE/ACM Workshop on Variability Modeling and Characterization

(VMC) 2018

November 8,  2018

San Diego, CA, USA

Registration through ICCAD

(Programs of previous workshops are available: 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008)

Call for Abstract: submission due on Oct. 15, 2018; acceptance on Oct. 22, 2018; accepted abstract will be presented at the poster session

Variability has emerged as a fundamental challenge to IC design in scaled CMOS technology; and it has profound impact on nearly all aspects of circuit performance. While some of the negative effects of variability can be handled via improvements in the manufacturing process, comprehensive methods are necessary to assess and manage the negative effects of variability, which in turn requires accurate and tractable variability models. The goal of the VMC workshop is to provide a forum for theoreticians and practitioners to freely exchange opinions on current practices as well as future research needs in variability modeling and characterization. In this year's edition of the VMC workshop, particular emphasis will be placed on the applications of machine-learning techniques to the variability topics listed below. The workshop organizers strongly encourage the submission of early results in the related topics. The submissions will be promptly evaluated and the author(s) of the accepted submissions are expected to present the results in a poster format preceded by a brief introductory presentation at the workshop. Distribution of Workshop Proceedings is limited to attendees.

Key Topics

Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\dot.jpg   Fundamental physics of device variability

Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\dot.jpg   Compact variability modeling development and applications

Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\dot.jpg   Statistical extraction of variability

Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\dot.jpg   Variability test structure design and calibration

Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\dot.jpg   Design interface with manufacturing and solutions for variability

Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\dot.jpg   Variability issues in emerging semiconductor technology

Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\dot.jpg   Temporal variability issues

Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\dot.jpg   Reliability considerations that may be closely related to variability

Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\dot.jpg   Variability in computing and systems

Agenda

1:00 – 1:10pm        Welcome Note and Opening Remarks: Abe Elfadel (Khalifa U., UAE)

1:10 – 1:50pm        Yiorgos Makris (UT Dallas, USA)

Machine Learning in Semiconductor Manufacturing and Test: Can Deep Learning Save The Day?

1:50 – 2:30pm        Zheng Zhang (UC Santa Barbara, USA)

Data-Efficient Machine Learning for Variation-Aware Design Automation: A Tensor Perspective

2:30 – 3:10pm        Youngsoo Shin (KAIST, Korea)

Lithography Optimizations through Machine Learning

3:10 – 3:50pm        Poster Pitches/Coffee Break/Poster Viewing

3:50 – 4:30pm        Mehdi Tahoori (Karlsruhe Institute of Technology, Germany)

Machine Learning for Variability Modeling and Mitigation of Energy-constrained Systems

4:30 – 5:10pm        Victor Kravets (IBM Research, USA)

Application of Boolean Sampling and Learning to the Error Localization and Correction in Semiconductor Designs

5:10 – 5:40pm        Panel: Machine Learning and VMC

5:40 – 6:00pm        Wrap-up and Closing Remarks: Abe Elfadel (Khalifa U., UAE)

Technical Program Committee

Co-chairs:   Abe Elfadel, Khalifa University, UAE

                     Takashi Sato, Kyoto University, Japan

                     Rasit O. Topaloglu, IBM, USA

 

Yu (Kevin) Cao, Arizona State University, USA

Chris Kim, University of Minnesota, USA

Colin McAndrew, Freescale Semiconductor, USA

Subhasish Mitra, Stanford University, USA

Hidetoshi Onodera, Kyoto University, Japan

David Z. Pan, University of Texas at Austin, USA

Vijay Reddy, Texas Instruments, USA

 

Sponsors:

 Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\acm.jpg           Description: Description: Description: Description: Description: Description: Description: C:\Paper Review\Modeling\2012\webpage\ieee.jpg                 

Last updated on Oct 26, 2018. Contents subject to change. All rights reserved.