Gi Hwan Choe

TL;DR: A novel machine learning (ML)-assisted methodology to analyze the variability of ferroelectric field-effect transistor (FeFET) with raw data from the metrology and trained a neural network using the polarization maps as inputs and the high/low threshold voltage, on-state current, and subthreshold slope as outputs.

TL;DR: In this paper , a machine learning-assisted approach is proposed for investigating the variability of ferroelectric field-effect transistor (FeFET) to shorten the loop of technology pathfinding.

TL;DR: This ML framework introduces a new approach for the DTCO of emerging devices, providing an efficient and accurate method to model and optimize their behavior while significantly reducing design iterations and time consumption.

TL;DR: High-voltage BEOL compatible oxide power transistors and superlattice capacitors for voltage conversion in H3D integrated circuits.