One-step variation included compact modeling with conditional variational autoencoder

Abstract

Efficient and accurate variation modeling serves as a critical part in circuit evaluation, which can reproduce actual electrical behavior of semiconductor devices. Conventional variation modeling usually consists of two steps: compact modeling the basic electrical properties and sub-modeling the variation sources introduced in MOSFET manufacturing process, mostly structural and doping parameters. This lengthy process results in a gap between device production and rapid circuit analysis. In order to improve modeling efficiency, in this work, we propose a one-step variation-included compact modeling approach leveraging machine learning. Utilizing conditional variational autoencoder (cVAE), currents with variation are directly constructed without the sub-modeling step, as variation sources of the cVAE model are automatically extracted. Benchmark against prior distribution of dataset generated by Monte Carlo simulation of BSIM-CMG, normalized variation in figure of merits (FoMs) of cVAE generated I-V curves are all above 0.9. After implementing the model in SPICE, high accuracy in circuit-level variation modeling also indicates the potential of the proposed model.