Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866917433947717632 |
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| author | Jeong, Gyujun Cho, Sungwon Shon, Minji Kim, Namhoon Hwang, Woohyun Seo, Kwangyou Lim, Suhwan Kim, Wanki Ha, Daewon Venkatesan, Prasanna Youn, Kihang Cherukuri, Ram Wang, Yiyi Datta, Suman Khan, Asif Yu, Shimeng |
| author_facet | Jeong, Gyujun Cho, Sungwon Shon, Minji Kim, Namhoon Hwang, Woohyun Seo, Kwangyou Lim, Suhwan Kim, Wanki Ha, Daewon Venkatesan, Prasanna Youn, Kihang Cherukuri, Ram Wang, Yiyi Datta, Suman Khan, Asif Yu, Shimeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06881 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model Jeong, Gyujun Cho, Sungwon Shon, Minji Kim, Namhoon Hwang, Woohyun Seo, Kwangyou Lim, Suhwan Kim, Wanki Ha, Daewon Venkatesan, Prasanna Youn, Kihang Cherukuri, Ram Wang, Yiyi Datta, Suman Khan, Asif Yu, Shimeng Machine Learning Artificial Intelligence Computational Physics 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. |
| title | Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model |
| topic | Machine Learning Artificial Intelligence Computational Physics |
| url | https://arxiv.org/abs/2603.06881 |