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AI model accelerates ferroelectric NAND retention analysis over 10000x faster than TCAD

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 1 source

Researchers have developed a novel Physics-Informed Neural Operator (PINO) model to accelerate the analysis of data retention in ferroelectric vertical NAND (Fe-VNAND) flash memory. This AI surrogate model integrates fundamental physical principles, achieving a speedup of over 10,000 times compared to traditional Technology Computer-Aided Design (TCAD) simulations. The PINO framework accurately predicts threshold voltage shifts and retention behavior, offering a significant advancement for optimizing device designs and enabling reliability-aware simulations. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Accelerates hardware simulation for memory devices, enabling faster design cycles and optimization.

RANK_REASON This is a research paper detailing a new AI model for hardware simulation.

Read on arXiv cs.LG →

COVERAGE [1]

  1. arXiv cs.LG TIER_1 · Gyujun Jeong (School of Electrical and Computer Engineering, Georgia Institute of Technology, GA, USA), Sungwon Cho (School of Electrical and Computer Engineering, Georgia Institute of Technology, GA, USA), Minji Shon (School of Electrical and Computer En ·

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

    arXiv:2603.06881v3 Announce Type: replace 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 …

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