Building a Post-Layout Simulation Performance Model with Global Mapping Model Fusion Technique  

作　　者：Zhikai Wang Wenfei Hu Sen Yin Ruitao Wang Jian Zhang Yan Wang Zuochang Ye 

机构地区：[1]School of Integrated Circuits,Tsinghua University,Beijing 100084,China [2]the Beijing Innovation Center for Future Chips(ICFC),Beijing National Research Center for Information Science and Technology(NRist),and School of Integrated Circuits,Tsinghua University,Beijing 100084,China

出　　处：《Tsinghua Science and Technology》2022年第3期512-525,共14页清华大学学报（自然科学版（英文版）

基　　金：supported by the National Key Technology Research and Development Program (Nos.2018YFB2202701 and 2019YFB2205003);the National Major Research Program from Ministry of Science and Technology of China (No. 2016YFA0201903);Science and Technology Program from Beijing Science and Technology Commission (No. Z201100004220003)。

摘　　要：Building a post-layout simulation performance model is essential in closing the loop of analog circuits, but it is a challenging task because of the high-dimensional space and expensive simulation cost. To facilitate efficient modeling, this paper proposes a Global Mapping Model Fusion(GMMF) technique. The key idea of GMMF is to reuse the schematic-level model trained by the Artificial Neural Network(ANN) algorithm, and combine it with few mapping coefficients to build the post-simulation model. Furthermore, as an efficient global optimization algorithm,differential evolution is applied to determine the optimal mapping coefficients with few samples. In GMMF, only a small number of mapping coefficients are unknown, so the number of post-layout samples needed is significantly reduced. To enhance practical utility of the proposed GMMF technique, two specific mapping relations, i.e., linear or weakly no-linear and nonlinear, are carefully considered in this paper. We conduct experiments on two topologies of two-stage operational amplifier and comparator in different commercial processes. All the simulation data for modeling are obtained from a parametric design framework. A more than 5 runtime speedup is achieved over ANN without surrendering any accuracy.

关 键 词：post-layout simulation performance model Global Mapping Model Fusion(GMMF) Artificial Neural Network(ANN) few mapping coefficients differential evolution 

分 类 号：TN40[电子电信—微电子学与固体电子学] TP18[自动化与计算机技术—控制理论与控制工程]