Ferroelectric transistor compact models train on measured current: spectral normalization smooths derivatives, and commercial SPICE runs large memory arrays
Compact models for ferroelectric field-effect transistors (FeFETs) have required either deriving physics-based equations term by term or training neural networks on smooth high-order derivatives from numerical solvers. This work trains a neural network on measured drain current instead, using scaled spectral normalization to constrain the network weights, smooth derivatives and filter measurement noise, covering the full range of polarization amplitudes and supporting dynamic polarization switching in transient simulations. Write and read operations of memory arrays up to large scale, including parasitics, complete transient simulation in commercial SPICE, and all converge.