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. This study demonstrates the model's effectiveness on a single FeFET configuration, serving as a pathway toward modeling the retention loss mechanisms.

翻译：基于铁电场效应晶体管（FeFET）的垂直NAND（Fe-VNAND）因具有更低编程电压，已成为克服z方向缩放限制的候选技术。然而，三维Fe-VNAND的数据保持能力受到电荷脱陷与铁电去极化复杂相互作用的制约。开发优化器件设计需探索广阔的参数空间，但传统技术计算机辅助设计（TCAD）工具的高计算成本使此类大规模优化难以实现。为突破这些仿真瓶颈，我们提出基于物理信息神经算子（PINO）的人工智能代理模型，用于高效预测阈值电压（Vth）漂移与保持特性。通过将基本物理原理嵌入学习架构，我们的PINO框架在保持物理精度的同时，相比TCAD实现了超过10000倍的加速。本研究在单个FeFET配置上验证了模型有效性，为建模保持损耗机制开辟了路径。