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DTSTAMP:20260402T024532Z
LOCATION:3004\, Level 3
DTSTART;TZID=America/Los_Angeles:20250623T110000
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UID:dac_DAC 2025_sess134_RESEARCH1355@linklings.com
SUMMARY:Generative Model Based Standard Cell Timing Library Characterizati
 on
DESCRIPTION:Hao-Yu Wu, Hsin-Tzu Chang, Shiuan-Yun Ding, and Iris Hui-Ru Ji
 ang (National Taiwan University) and Benson Tsao, Vinson Wu, and Wei-Kai S
 hih (Synopsys)\n\nAccurate cell timing characterization is essential, on w
 hich static timing analysis relies to verify timing performance and ensure
  design robustness across various PVT conditions (corners). The corner exp
 losion in modern design amplifies the efficiency and scalability challenge
  for accurate characterization. However, the conventional characterization
  approach of SPICE simulation alone becomes prohibitively expensive due to
  the increasing computational complexity and the amount of characterized d
 ata. In this paper, we view the characterization problem from a generative
  modeling perspective to tackle the efficiency and scalability challenge. 
 With a hybrid of generative adversarial network (GAN) and autoencoder, our
  generative model learns and generalizes among various timing arcs and cor
 ners. Experimental results demonstrate that the proposed framework achieve
 s high accuracy and extensibility while reducing the runtime significantly
 .\n\nTopics: EDA\n\nTracks: EDA3: Timing Analysis and Optimization\n\nSess
 ion Chairs: Zhiyao Xie (Hong Kong University of Science and Technology (HK
 UST)) and Xin Zhao (IBM)\n\n
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