Development of Knowledge-Based Artificial Neural Networks for Analysis of PSIJ in CMOS Inverter Circuits

Abstract

In this article, a knowledge-based artificial neural network (ANN) is developed for predicting jitter in CMOS inverter circuits in the presence of power supply noise (PSN). The proposed ANN provides for efficient training in a hybrid approach using input data extracted from both analytical closed-form expressions and a circuit simulator. The proposed ANN demonstrates a reasonably accurate prediction of power supply-induced jitter (PSIJ) with results that closely match that from directly using a circuit simulator (HSPICE) for a case study with 22-nm CMOS technology.