典型文献
Building a Post-Layout Simulation Performance Model with Global Mapping Model Fusion Technique
文献摘要:
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 5x runtime speedup is achieved over ANN without surrendering any accuracy.
文献关键词:
中图分类号:
作者姓名:
Zhikai Wang;Wenfei Hu;Sen Yin;Ruitao Wang;Jian Zhang;Yan Wang;Zuochang Ye
作者机构:
School of Integrated Circuits,Tsinghua University,Beijing 100084,China;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
文献出处:
引用格式:
[1]Zhikai Wang;Wenfei Hu;Sen Yin;Ruitao Wang;Jian Zhang;Yan Wang;Zuochang Ye-.Building a Post-Layout Simulation Performance Model with Global Mapping Model Fusion Technique)[J].清华大学学报自然科学版(英文版),2022(03):512-525
A类:
GMMF,surrendering
B类:
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AB值:
0.598292
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