Overview of emerging semiconductor device model methodologies: From device physics to machine learning engines

Abstract

Advancements in the semiconductor industry introduce novel channel materials, device structures, and integration methods, leading to intricate physics challenges when characterizing devices at circuit level. Nevertheless, accurate models for emerging devices are crucial for physics-driven TCAD-to-SPICE flows to enable the increasingly vital design technology co-optimization (DTCO). Particularly for ultra-scaled devices where quantum effects become significant, this led to the introduction of empirical model parameters and a disconnection to manufacturing processes. To catch up with these developments, an alternative to the traditional white-box modeling methods has attracted much attention: machine learning-assisted compact modeling (MLCM). These black-box methods target towards general-purpose modeling of complex mathematics and physics through training of neural networks on experimental and simulated data, generating an accurate closed-form mapping between output characteristics and input parameters for fabrication process and device operation. To address this new trend, this work provides a comprehensive overview of emerging device model methodologies, spanning from device physics to machine learning engines. By analyzing, structuring, and extending distributed efforts on this topic, it is shown how MLCM can overcome limitations of traditional compact modeling and contribute to effective DTCO to further advance semiconductor technologies.