New-Generation Design-Technology Co-Optimization (DTCO): Machine-Learning Assisted Modeling Framework

TL;DR

This paper introduces a machine-learning based modeling framework for design-technology co-optimization, using neural networks to predict device and circuit characteristics in FinFETs and TFETs, enhancing accuracy without prior device physics knowledge.

Contribution

The paper presents a novel ML-assisted modeling framework for DTCO that replaces traditional compact models with neural networks, applicable to multiple device types.

Findings

High prediction accuracy in FinFET device and circuit modeling.

Successful application to tunnel FET (TFET) devices.

Provides a new approach for device modeling in DTCO flow.

Abstract

In this paper, we propose a machine-learning assisted modeling framework in design-technology co-optimization (DTCO) flow. Neural network (NN) based surrogate model is used as an alternative of compact model of new devices without prior knowledge of device physics to predict device and circuit electrical characteristics. This modeling framework is demonstrated and verified in FinFET with high predicted accuracy in device and circuit level. Details about the data handling and prediction results are discussed. Moreover, same framework is applied to new mechanism device tunnel FET (TFET) to predict device and circuit characteristics. This work provides new modeling method for DTCO flow.