AI-Assisted Design Automation of Circular and Asymmetric Inductor in CMOS Technology

This paper presents a methodology of the automated inductor generator for radio-frequency (RF) integrated circuit. We propose a machine learning assisted parameterized-cell (PCELL) generation method. This method supports polygonal, circular and asymmetric inductor shapes. The proposed PCELL has been validated in 65-nm CMOS 1P9M process and analyzed in terms of inductance (L), quality factor (Q factor) and area. An artificial-neural-network (ANN) based regression model is trained to reduce the EM based inductor design cost such as ×530 less simulation time. According to the target inductor design optimization, 1 GHz operating frequency, we achieve the relative error rates of 0.63% and 1.73% for L and Q factor, respectively.