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

• URL: http://arxiv.org/abs/2603.06881v1
• Date: Fri, 06 Mar 2026 21:01:31 GMT
• Title: Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model
• Abstract summary: 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.<n>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.
• Score: 1.8836299537031278
• License: http://creativecommons.org/licenses/by/4.0/
• 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. This study demonstrates the model's effectiveness on a single FeFET configuration, serving as a pathway toward modeling the retention loss mechanisms.

Related papers

• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries [47.27974914502511]
  We introduce a deep learning surrogate pipeline based on the Swin3D Transformer to predict discharge dynamics directly from data.<n>The proposed method reduces the computational overhead by orders of magnitude, providing a scalable and efficient framework for high- throughput battery design and optimization.
  arXiv  Detail & Related papers  (2026-07-22T09:10:48Z)
• SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction [46.645872209565574]
  We present a specialized large-scale foundation model for virtual cell perturbation prediction.<n>First, we build a BioNeMo-based training and inference framework that substantially improves data throughput.<n>Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture.<n>Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics.
  arXiv  Detail & Related papers  (2026-03-18T05:51:08Z)
• Gradient Networks for Universal Magnetic Modeling of Synchronous Machines [39.146761527401424]
  This paper presents a physics-informed neural network approach for dynamic modeling of saturable synchronous machines.<n>We introduce an architecture that incorporates gradient networks directly into the fundamental machine equations.<n>We validate the proposed approach using measured and finite-element method (FEM) datasets from a 5.6-kW permanent-magnet synchronous machine.
  arXiv  Detail & Related papers  (2026-02-16T17:28:42Z)
• Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction [8.953088273349499]
  Physics-informed neural networks (PINNs) integrate physical principles into machine learning models.<n>PINNs embed physical principles only in loss functions, leading to slow convergence, optimization instability, and spectral bias.<n>This work introduces architecture physics embedded (PE)-PINN, which integrates additional physical guidance directly into the neural network architecture.
  arXiv  Detail & Related papers  (2026-02-13T04:28:23Z)
• Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation [29.49941497527361]
  PINNs present a transformative approach for smart grid modeling by integrating physical laws directly into learning frameworks.<n>This paper evaluates PINNs' capabilities as surrogate models for smart grid dynamics.<n>We demonstrate PINNs' superior generalization, outperforming data-driven models in error reduction.
  arXiv  Detail & Related papers  (2025-08-29T12:15:32Z)
• Modeling Membrane Degradation in PEM Electrolyzers with Physics-Informed Neural Networks [45.32169712547367]
  Proton exchange membrane (PEM) electrolyzers are pivotal for sustainable hydrogen production.<n>Their long-term performance is hindered by membrane degradation, which poses reliability and safety challenges.<n>Traditional physics-based models have been developed, offering interpretability but requiring numerous parameters that are often difficult to measure and calibrate.<n>This study presents the first application of Physics-Informed Neural Networks (PINNs) to model membrane degradation in PEM electrolyzers.
  arXiv  Detail & Related papers  (2025-06-19T15:46:49Z)
• OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics [25.066485418709114]
  We propose OmniFluids, a pure physics pre-trained model that captures fundamental fluid dynamics laws and adapts efficiently to diverse downstream tasks.<n>We develop a training framework combining physics-only pre-training, coarse-grid operator distillation, and few-shot fine-tuning.<n>Tests show that OmniFluids outperforms state-of-the-art AI-driven methods in terms of flow field prediction and statistics.
  arXiv  Detail & Related papers  (2025-06-12T16:23:02Z)
• Balancing Robustness and Efficiency in Embedded DNNs Through Activation Function Selection [1.474723404975345]
  Machine learning-based embedded systems for safety-critical applications must be robust to perturbations caused by soft errors.<n>We focus on encoder-decoder convolutional models developed for semantic segmentation of hyperspectral images with application to autonomous driving systems.
  arXiv  Detail & Related papers  (2025-04-07T14:21:31Z)
• Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling [0.0]
  Physics-Informed Neural Networks (PINNs) offer a promising approach by incorporating physical laws directly into the learning process. This article explores the capabilities and limitations of the DeepXDE framework, a tool specifically designed for implementing PINNs. We show that applying a logarithmic transformation to the current (ln(I)) significantly enhances the stability and accuracy of PINN predictions.
  arXiv  Detail & Related papers  (2024-11-13T19:08:36Z)
• Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies [41.994460245857404]
  This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources.<n>We develop a physics-based AI foundation model with high-resolution synchro-waveform measurement technology to enhance grid resilience and reduce economic losses from outages.
  arXiv  Detail & Related papers  (2024-03-11T17:28:46Z)
• Deep Learning Algorithm for Advanced Level-3 Inverse-Modeling of Silicon-Carbide Power MOSFET Devices [43.99494688206196]
  Inverse device modelling is suitable to reconstruct drifted physical parameters of devices temporally degraded or to retrieve physical configuration. The key application of SiC power devices is in the automotive field. The aim of this work is to provide a possible deep learning-based solution for retrieving physical parameters of the SiC Power.
  arXiv  Detail & Related papers  (2023-10-16T08:07:40Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Multilayer Perceptron Based Stress Evolution Analysis under DC Current Stressing for Multi-segment Wires [8.115870370527324]
  Electromigration (EM) is one of the major concerns in the reliability analysis of very large scale integration (VLSI) systems. Traditional methods are often not sufficiently accurate, leading to undesirable over-design especially in advanced technology nodes. We propose an approach using multilayer perceptrons (MLP) to compute stress evolution in the interconnect trees during the void nucleation phase.
  arXiv  Detail & Related papers  (2022-05-17T07:38:20Z)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.