Towards Designing a Unified DNN Architecture for Analog and Mixed-Signal Circuit Characterization

Analog circuit characterization is a pivotal phase in the design process, serving to validate their performance. It ensures that design choices lead to optimization, ultimately resulting in reliable and robust designs. The conventional, resource-intensive circuit characterization using traditional simulation tools is being supplanted by Machine Learning surrogate models, providing computational advantages. Nevertheless, these models tend to be specific to particular circuits, demanding significant design efforts and often lacking reusability for other designs and topologies. In this paper, we introduce a Unified Neural Network Architecture that is versatile and computationally efficient. It offers a streamlined and effective approach for modeling a diverse array of analog circuits prone to process, voltage, and temperature variations. Experimental trials conducted on various circuits in CMOS 180nm, 65nm and 28nm technologies demonstrate a mean average percentage error of less than 1%, affirming the effectiveness and reusability of the proposed architecture. This leads to substantial savings in design time, computational resources, and characterization costs.