{"title":"Investigation of Random Telegraph Noise Scaling Dependency in 3-D NAND Using Monte Carlo Simulator","authors":"Eunseok Oh;Hyungcheol Shin","doi":"10.1109/TED.2025.3542008","DOIUrl":null,"url":null,"abstract":"Random telegraph noise (RTN) shifts the threshold voltage (<inline-formula> <tex-math>${V} _{t}$ </tex-math></inline-formula>) of 3-D <sc>nand</small> flash memory cells, making it a major cause of device malfunction. As device scaling continues, RTN has become an increasingly significant factor affecting device performance. The aim of this study is to develop a simulator that predicts the distribution of <inline-formula> <tex-math>${V} _{t}$ </tex-math></inline-formula> shifts induced by RTN in scaled 3-D <sc>nand</small> flash memory. Previous RTN analysis methods rely heavily on numerous simulations or measurements, which are not only time-consuming but also limited in predicting the effects of device scaling on RTN-induced <inline-formula> <tex-math>${V} _{t}$ </tex-math></inline-formula> shifts. To address these limitations, we developed a novel RTN Monte Carlo simulator that integrates a previously developed artificial neural network (ANN)-based machine learning (ML) model with a Markov process for trap occupancy states. Using this simulator, we comprehensively analyzed RTN effects in 3-D <sc>nand</small> devices with multiple traps, extracted the corresponding decay constants (<inline-formula> <tex-math>$\\lambda $ </tex-math></inline-formula>), and modeled the dependence of <inline-formula> <tex-math>$\\lambda $ </tex-math></inline-formula> on device physical parameters. The simulator provides flexibility in generating large-scale RTN data without the need for additional simulations or measurements, significantly reducing computation time while maintaining accuracy.","PeriodicalId":13092,"journal":{"name":"IEEE Transactions on Electron Devices","volume":"72 4","pages":"1750-1755"},"PeriodicalIF":2.9000,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Electron Devices","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10904002/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Random telegraph noise (RTN) shifts the threshold voltage (${V} _{t}$ ) of 3-D nand flash memory cells, making it a major cause of device malfunction. As device scaling continues, RTN has become an increasingly significant factor affecting device performance. The aim of this study is to develop a simulator that predicts the distribution of ${V} _{t}$ shifts induced by RTN in scaled 3-D nand flash memory. Previous RTN analysis methods rely heavily on numerous simulations or measurements, which are not only time-consuming but also limited in predicting the effects of device scaling on RTN-induced ${V} _{t}$ shifts. To address these limitations, we developed a novel RTN Monte Carlo simulator that integrates a previously developed artificial neural network (ANN)-based machine learning (ML) model with a Markov process for trap occupancy states. Using this simulator, we comprehensively analyzed RTN effects in 3-D nand devices with multiple traps, extracted the corresponding decay constants ($\lambda $ ), and modeled the dependence of $\lambda $ on device physical parameters. The simulator provides flexibility in generating large-scale RTN data without the need for additional simulations or measurements, significantly reducing computation time while maintaining accuracy.
期刊介绍:
IEEE Transactions on Electron Devices publishes original and significant contributions relating to the theory, modeling, design, performance and reliability of electron and ion integrated circuit devices and interconnects, involving insulators, metals, organic materials, micro-plasmas, semiconductors, quantum-effect structures, vacuum devices, and emerging materials with applications in bioelectronics, biomedical electronics, computation, communications, displays, microelectromechanics, imaging, micro-actuators, nanoelectronics, optoelectronics, photovoltaics, power ICs and micro-sensors. Tutorial and review papers on these subjects are also published and occasional special issues appear to present a collection of papers which treat particular areas in more depth and breadth.