Presentation
A Physics-Prior Intelligent Compact Modeling Framework for BEOL-Compatible DTCO: EKAN-Based Distillation from Neural to Symbolic Models
DescriptionNeural compact models are increasingly explored for design–technology co-optimization (DTCO), yet their black-box nature hinders physical interpretability and seamless SPICE deployment. We introduce a physics-prior neural-to-symbolic compact modeling framework based on Efficient Kolmogorov–Arnold Networks (EKAN) trained on multidimensional oxide-FET data. EKAN first learns a smooth, bias-aware log-current surrogate; its spline activations are then distilled into a closed-form current expression via KAN-derived one-dimensional atoms, physics-guided feature libraries, and weighted sparse regression with monotonicity regularization. The resulting Verilog-A model is SPICE-ready, preserves key device trends across bias and process, and attains accuracy comparable to neural compact models while remaining interpretable.
Event Type
Research Manuscript
TimeTuesday, July 282:08pm - 2:21pm PDT
LocationMtg Room 101B