PiSPICE: Accelerating Post-Layout SPICE Simulation via Essential Parasitic Identification
Zhou Jin, Jing Li, Jian Xin, Tianjia Zhou, Xiao Wu, Dan Niu, Zuochang Ye
摘要
As process nodes scale to more advanced technologies, post-layout simulations for integrated circuits have become increasingly complex, involving billions to trillions of nodes. The growing design complexity and transistor integration require more accurate and efficient post-layout SPICE simulations. However, existing methods for solving large-scale post-layout circuits face significant challenges due to high computational costs. In this paper, we propose a new approach, PiSPICE, which utilizes adjoint sensitivity analysis to identify critical parasitics and eliminate non-critical ones, effectively reducing the simulation scale and improving speed. By modeling parasitics and performing sensitivity analysis on pre-layout circuits, we significantly reduce the computational burden and avoid the overhead of directly analyzing sensitivities in large-scale postlayout circuits. By retaining only the critical parasitics and applying model order reduction to minimize their impact, while eliminating non-critical parasitics, PiSPICE achieves a speedup of up to 17.27 x in simulation with an error margin of less than 0.78% compared to the commercial simulator Spectre.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
相关 Paper
- MemSens: Significantly Reducing Memory Overhead in Adjoint Sensitivity Analysis Using Novel Error-Bounded Lossy CompressionChenxi Li, Yihang Feng, Fuxing Deng, Dingwen Tao 等DAC 2025
- MASC: A Memory-Efficient Adjoint Sensitivity Analysis through Compression Using Novel Spatiotemporal PredictionChenxi Li, Boyuan Zhang, Yongqiang Duan, Yang Li 等DAC 2024 · 被引用 4 次
- Sensitivity Importance Sampling Yield Analysis and Optimization for High Sigma Failure Rate EstimationWenfei Hu, Zhikai Wang, Sen Yin, Zuochang Ye 等DAC 2021 · 被引用 13 次
- GTN-Path: Efficient Path Timing Prediction through Waveform Propagation with Graph TransformerLihao Liu, Beisi Lu, Yunhui Li, Li Shang 等DAC 2025 · 被引用 2 次
- A Provably Good and Practically Efficient Algorithm for Common Path Pessimism Removal in Large DesignsZizheng Guo, Tsung-Wei Huang, Yibo LinDAC 2021 · 被引用 31 次
