ANFIS Based Thermal Estimation of Ultradeep Submicron Digital Circuit Design

Abstract

In this paper, the use of the Adaptive Neuro Fuzzy Inference System (ANFIS) to model the CMOS inverter is discussed as a tool for developing and simulating CMOS logic circuits at the ultradeep submicron technology node of 22nm. The ANFIS structures are built and trained using MATLAB software. The ANFIS network was trained using data obtained from the analytical model (at 298.15K and 398.15K). For training, two methodologies are used: a hybrid learning method based on back-propagation and least-squares estimation, and back-propagation. The effect of the ANFIS model's structure on the accuracy and performance of the CMOS inverter has also been investigated. Further, simulation through HSPICE using (Predictive Technology Model) PTM nominal parameters has been done to compare with ANFIS (trained using an analytical model) results. The comparison of ANFIS and HSPICE suggests the ANFIS modelling procedure's practicality and correctness. The findings demonstrate that the ANFIS simulation is significantly faster and more comparable than the HSPICE simulation and that it can be easily integrated into software tools for designing and simulating complicated CMOS logic circuits.