{"title":"Prediction of Random Telegraph Noise-Induced Threshold Voltage Shift and Its Scaling Dependency Using Machine Learning","authors":"Eunseok Oh;Hyungcheol Shin","doi":"10.1109/JEDS.2024.3471999","DOIUrl":null,"url":null,"abstract":"Random telegraph noise (RTN) shifts the threshold voltage (Vt) of 3D NAND flash memory cells, making it a key factor of the device malfunction. The aim of this study is to predict the distribution of RTN induced \n<inline-formula> <tex-math>${\\mathrm { V}}_{\\mathrm { t}}$ </tex-math></inline-formula>\n shift in 3D NAND flash memory. Artificial neural network (ANN)-based machine learning (ML) is used for this prediction. With 2000 samples, ANN is trained and tested to predict the \n<inline-formula> <tex-math>${\\mathrm { V}}_{\\mathrm { t}}$ </tex-math></inline-formula>\n shift of random cells with high reliability. Furthermore, ANN is applied to predict the tendency of RTN-induced \n<inline-formula> <tex-math>${\\mathrm { V}}_{\\mathrm { t}}$ </tex-math></inline-formula>\n shift in scaled 3D NAND. Compared to prior works which has required far more measurements or simulations, the predictions are shown to shorten the time spent to obtain the distribution. Based on these predictions, the dependency of the decay constant on cell variation is investigated, which is a most critical parameter in analyzing the RTN distribution. This indicates that it is possible to apply ANN-based ML to predict various characteristics of 3D NAND flash memory in a much shorter time and to develop numerical models of related parameters.","PeriodicalId":13210,"journal":{"name":"IEEE Journal of the Electron Devices Society","volume":"12 ","pages":"934-940"},"PeriodicalIF":2.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10702511","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of the Electron Devices Society","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10702511/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Random telegraph noise (RTN) shifts the threshold voltage (Vt) of 3D NAND flash memory cells, making it a key factor of the device malfunction. The aim of this study is to predict the distribution of RTN induced
${\mathrm { V}}_{\mathrm { t}}$
shift in 3D NAND flash memory. Artificial neural network (ANN)-based machine learning (ML) is used for this prediction. With 2000 samples, ANN is trained and tested to predict the
${\mathrm { V}}_{\mathrm { t}}$
shift of random cells with high reliability. Furthermore, ANN is applied to predict the tendency of RTN-induced
${\mathrm { V}}_{\mathrm { t}}$
shift in scaled 3D NAND. Compared to prior works which has required far more measurements or simulations, the predictions are shown to shorten the time spent to obtain the distribution. Based on these predictions, the dependency of the decay constant on cell variation is investigated, which is a most critical parameter in analyzing the RTN distribution. This indicates that it is possible to apply ANN-based ML to predict various characteristics of 3D NAND flash memory in a much shorter time and to develop numerical models of related parameters.
期刊介绍:
The IEEE Journal of the Electron Devices Society (J-EDS) is an open-access, fully electronic scientific journal publishing papers ranging from fundamental to applied research that are scientifically rigorous and relevant to electron devices. The J-EDS publishes original and significant contributions relating to the theory, modelling, 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, nanodevices, optoelectronics, photovoltaics, power IC''s, and micro-sensors. Tutorial and review papers on these subjects are, also, published. And, occasionally special issues with a collection of papers on particular areas in more depth and breadth are, also, published. J-EDS publishes all papers that are judged to be technically valid and original.