Multiobjective Design of 2-D-Material-Based Field-Effect Transistors With Machine Learning Methods

Abstract

Design optimization of emerging nanoscale transistor technologies often requires careful design tradeoff between many objectives, including speed, power, variability, and so on. By leveraging machine learning (ML) methods, we develop a multiobjective optimization (MOO) framework for 2-D-material-based field-effect transistors (FETs) near the scaling limit. The MOO design framework performs gradient-free efficient global optimization and offers the option of using active learning. Optimum designs with a tradeoff between transistor speed, power, and variability are identified automatically for transition metal dichalcogenide (TMDC) and black phosphorene FETs by applying the MOO design framework that couples ML methods to quantum transport device simulations. The design optimization results show that the International Roadmap of Devices and Systems (IRDS) target of 2025 and 2028 technology nodes can be met by 2-D FETs.