Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model

TL;DR

This paper introduces a physics-informed AI surrogate model that accelerates the simulation of retention behavior in ferroelectric vertical NAND devices by over 10,000 times, enabling efficient device optimization.

Contribution

The authors develop a PINO-based neural surrogate that embeds physical principles, drastically reducing simulation time while maintaining accuracy for FeFET retention analysis.

Findings

Achieves over 10,000x speedup compared to TCAD simulations.

Provides a physics-consistent data engine for device modeling and reliability analysis.

Demonstrates effectiveness on a single FeFET configuration.

Abstract

Ferroelectric field-effect transistors (FeFET)-based vertical NAND (Fe-VNAND) has emerged as a promising candidate to overcome z-scaling limitations with lower programming voltages. However, the data retention of 3D Fe-VNAND is hindered by the complex interaction between charge detrapping and ferroelectric depolarization. Developing optimized device designs requires exploring an extensive parameter space, but the high computational cost of conventional Technology Computer-Aided Design (TCAD) tools makes such wide-scale optimization impractical. To overcome these simulation barriers, we present a Physics-Informed Neural Operator (PINO)-based AI surrogate model designed for high-efficiency prediction of threshold voltage (Vth) shifts and retention behavior. By embedding fundamental physical principles into the learning architecture, our PINO framework achieves a speedup exceeding 10000x compared to TCAD while maintaining physical accuracy. The resulting surrogate provides a physics-consistent data engine for compact model parameter extraction and look-up-table (LUT) generation, directly supporting reliability-aware SPICE simulation of Fe-VNAND. This study demonstrates the model's effectiveness on a single FeFET configuration, serving as a pathway toward modeling the retention loss mechanisms.