Deep-Learning-Assisted Physics-Driven MOSFET Current-Voltage Modeling
Ming-Yen Kao, Hei Kam, Chenming Hu
Abstract
In this work, we propose using deep learning to improve the accuracy of the partially-physics-based conventional MOSFET current-voltage model. The benefits of having some physics-driven features in the model are discussed. Using a portion of the Berkeley Short-channel IGFET Common-Multi-Gate (BSIM-CMG), the industry-standard FinFET and GAAFET compact model, as the physics model and a 3-layer neural network with 6 neurons per layer, the resultant model can well predict IV, output conductance, and transconductance of a TCAD-simulated gate-all-around transistor (GAAFET) with outstanding 3-sigma errors of 1.3%, 4.1%, and 2.9%, respectively. Implications for circuit simulation are also discussed.
Topics & Concepts
TransconductanceMOSFETTransistorSemiconductor device modelingThreshold voltageArtificial neural networkSigmaVoltageCurrent (fluid)Logic gatePhysicsElectronic engineeringConductanceElectrical engineeringOptoelectronicsComputer scienceCMOSEngineeringArtificial intelligenceQuantum mechanicsCondensed matter physicsAdvancements in Semiconductor Devices and Circuit DesignSemiconductor materials and devicesSilicon Carbide Semiconductor Technologies