Machine Learning-based Inverse Design Model of a Transistor

Abstract

Objectives: To develop an inverse design model for transistors, utilizing machine learning algorithms to predict key design parameters specifically, the length and width based on specified gain and bandwidth requirements. And to conduct a comprehensive comparative analysis with existing literature, evaluating the efficacy and novelty of the proposed model in the context of semiconductor engineering challenges and methodologies. Methods: The comprehensive dataset, comprising 30,000 values generated through LTspice simulations, forms the basis for training the machine learning model. Utilizing a Random Forest regressor as the base model and a multi-output regressor as the main model, the project involves extensive data analysis, model development, and iterative fine-tuning. Findings: The outcomes demonstrate the efficacy of the developed model in accurately predicting transistor dimensions. Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared, highlight the precision of the model in fulfilling the specified objectives. Novelty: This study introduces a novel approach to semiconductor device design optimization, showcasing the potential of machine learning to streamline the inverse design process. The use of a multi-output regressor, feature engineering, and fine-tuning through log transformation contribute to the innovative nature of the developed model. Keywords: Machine Learning (ML) model, Random Forest regressor, multi­output regressor, Feature engineering, Fine­tuning