MOBO-Driven Advanced Sub-3-nm Device Optimization for Enhanced PDP Performance

Optimizing the nonlinear electrical characteristics of sub-3-nm devices requires considerable trial and error. However, due to the complexity of physics and secondary effects, technology computer-aided design (TCAD) simulations are time-consuming. Even with a combination of TCAD and a suitable design of experiments (DOEs), comprehensive exploration of the design space using TCAD is a challenging task. In this study, we propose a device optimization framework that can dramatically reduce the number of TCAD simulations while identifying the optimal device structure. The framework we propose consists of an artificial neural network (ANN)-based objective function derived from a dataset generated by weighted Sobol sampling, a multiobjective Bayesian optimization (MOBO) model for device optimization, and an ANN-based compact model for circuit simulation. The framework produced a device structure that showed a 51.5% performance improvement compared to the best device performance found from individual TCAD simulations of 128 structures. In contrast, the manual determination of a device achieving similar results required more than 2048 TCAD simulations.