DC-Model: A New Method for Assisting the Analog Circuit Optimization

Yuan Wang, Jian Xin, Haixu Liu, Qian Qin, Chenkai Chai, Yukai Lu, Jinglei Hao, Jianhao Xiao, Zuochang Ye, Yan Wang
Tsinghua University


Both in academia and industry, a series of design methodologies based on evolutionary algorithms or machine learning techniques have been proposed to solve the problem of analog device sizing. However, these methods typically need a large number of circuit simulations during the optimization process and these simulations significantly increase the learning and computational costs. To tackle this problem, in this work, we propose DC-Model, a DC simulation-based neural network model that can greatly reduces the whole simulation time while been applied in the field of analog circuit optimization. DC-Model is inspired by the relationship between MOSFET dc operating point output parameters and circuit performances.