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Stochastic Modeling and Validation of Analog/Mixed-Signal Circuits under Process Variations

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What
  • PhD Defenses
When Nov 19, 2012
from 01:00 PM to 03:00 PM
Where Engr. IV Bldg. Tesla Room 53-125
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Fang Gong

Advisor: Professor Lei He


Abstract

In the past few decades, the semiconductor industry keeps scaling down the feature size of CMOS transistors with great efforts to provide faster devices and higher integration density, which follows the Moore’s law. However, as integrated circuit (IC) designs are approaching processes below 45nm, the increasing variability from manufacturing process (e.g., chemical mechanical polishing (CMP), etching, and lithography) can significantly change the circuit behavior and lead to great yield loss. Therefore, it is urgently sought to accurately model and efficiently analyze the impact of process variations on analog/mixed-signal (AMS) circuits.

In this research the process variation issues in yield estimation, variability-aware behavioral modeling and failure analysis of memory circuits are studied and novel solutions are presented. A fast algorithm, QuickYield, is proposed to estimate the yield rate of AMS circuits under process variations. In addition, a novel moment-matching based method has been proposed to accurately extract the “arbitrary” probabilistic behavioral distributions of AMS circuits. Moreover, an improved importance sampling based algorithm is presented to efficiently estimate the probability of “rare failure events” for SRAM cells, which provides high accuracy along with significant speedup over other available methods. These modeling and validation methodologies can be used to accurately analyze the AMS circuit designs under process variations in the present nano-technology era and future generation of integrated circuits and systems.

 

Biography

Fang Gong is a PhD candidate at the department of Electrical Engineering at UCLA.  He earned the B.S. degree from Beijing University of Aeronautics and Astronautics and M.S. degree from Tsinghua University in China, both in Computer Science. His research focuses on probabilistic modeling and statistical analysis with applications to circuits, healthcare and energy systems. He has published 20 technical papers and been selected as a recipient of the prestigious IBM Ph.D. fellowship in 2012 that honors his outstanding research on variation-aware modeling and analysis for VLSI circuits.

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