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

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.