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 ...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.展开更多
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)。
文摘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.
