@article{Wang2022, 
author = {Zhikai Wang and Wenfei Hu and Sen Yin and Ruitao Wang and Jian Zhang and Yan Wang and Zuochang Ye},
title = {Building a Post-Layout Simulation Performance Model with Global Mapping Model Fusion Technique},
year = {2022},
journal = {Tsinghua Science and Technology},
volume = {27},
number = {3},
pages = {512-525},
keywords = {post-layout simulation performance model, Global Mapping Model Fusion (GMMF), Artificial Neural Network (ANN), few mapping coefficients, differential evolution},
url = {https://www.sciopen.com/article/10.26599/TST.2021.9010042},
doi = {10.26599/TST.2021.9010042},
abstract = {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.}
}