用物理约束的AI模型，把铁电闪存模拟速度提升一万倍
Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model
- 基于物理信息神经算子构建仿真代理模型
- 比传统工具快10000倍，且保持物理准确性
- 适合芯片可靠性设计与快速仿真需求
基于铁电场效应晶体管（FeFET）的垂直闪存（Fe-VNAND）有望突破传统缩放极限，实现更低编程电压。然而，3D Fe-VNAND的数据保持性能受电荷消散与铁电退极化复杂相互作用制约。传统技术计算机辅助设计（TCAD）工具计算成本高，难以支持大规模参数空间优化。为此，本文提出一种物理信息神经算子（PINO）驱动的AI代理模型，可高效预测阈值电压（Vth）漂移与保持特性。通过将基本物理规律嵌入学习架构，该模型相比TCAD实现超10000倍加速，同时保持物理一致性。生成的代理模型可作为紧凑模型参数提取与查找表（LUT）生成的数据引擎，直接支持面向可靠性的Fe-VNAND SPICE仿真。本研究在单个FeFET结构上验证了模型有效性，为建模保持退化机制提供新路径。
原文摘要 · Abstract (English)
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.
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