Modeling Emerging Technologies using Machine Learning: Challenges and Opportunities Invited Talk

Compact models of transistors act as the link between semiconductor technology and circuit design via circuit simulations. Unfortunately, compact model development and calibration is a challenging and time-intensive task, hindering rapid prototyping of a circuit (via circuit simulations) in emerging technologies. Moreover, foundries want to protect their confidential technology details to prevent reverse engineering. Hence, they limit access to compact transistor models of commercial technologies (e.g., with Non-Disclosure-Agreements). In this work, we propose Machine Learning (ML) to bridge the gap between early device measurements and later occurring compact model development. Our approach employs a Neural Network (NN) that captures the electrical response of a conventional FinFET transistor without knowledge of semiconductor physics. Additionally, our approach can be applied to emerging technologies, using Negative Capacitance FinFET (NC-FinFET) as an example for a (challenging to model) emerging technology. Inherently, the black-box nature of ML approaches keeps technology manufacturing details confidential. Furthermore, we show how using solely R2 score as our fitness function is insufficient and instead propose fitness based on key electrical characteristics or transistors like threshold voltage. Our NN-based transistor modeling can infer FinFET and NC-FinFET with an R2 score larger than 0.99 and transistor characteristics within 5% of experimental data.