Pub Date : 2024-08-21DOI: 10.1109/JEDS.2024.3447150
Gerardo Malavena;Salvatore M. Amoroso;Andrew R. Brown;Plamen Asenov;Xi-Wei Lin;Victor Moroz;Mattia Giulianini;David Refaldi;Christian Monzio Compagnoni;Alessandro S. Spinelli
In Part II of this article we discuss the impact of a discrete treatment of traps on 3-D NAND Flash random telegraph noise (RTN). A higher RTN results when discrete traps are taken into account, that can only be explained by a stronger influence of the discrete charged traps on the current conduction, leading to more percolation. The effects are then investigated as a function of the cell parameters, showing that a continuous model for traps cannot reproduce the correct dependence.
{"title":"Discrete-Trap Effects on 3-D NAND Variability – Part II: Random Telegraph Noise","authors":"Gerardo Malavena;Salvatore M. Amoroso;Andrew R. Brown;Plamen Asenov;Xi-Wei Lin;Victor Moroz;Mattia Giulianini;David Refaldi;Christian Monzio Compagnoni;Alessandro S. Spinelli","doi":"10.1109/JEDS.2024.3447150","DOIUrl":"https://doi.org/10.1109/JEDS.2024.3447150","url":null,"abstract":"In Part II of this article we discuss the impact of a discrete treatment of traps on 3-D NAND Flash random telegraph noise (RTN). A higher RTN results when discrete traps are taken into account, that can only be explained by a stronger influence of the discrete charged traps on the current conduction, leading to more percolation. The effects are then investigated as a function of the cell parameters, showing that a continuous model for traps cannot reproduce the correct dependence.","PeriodicalId":13210,"journal":{"name":"IEEE Journal of the Electron Devices Society","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643403","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-21DOI: 10.1109/JEDS.2024.3447032
Xiaoying Tang;Zhiqiang Li;Lang Zeng;Hongwei Zhou;Xiaoxu Cheng;Zhenjie Yao
Engineers used TCAD tools for semiconductor devices modeling. However, it is computationally expensive and time-consuming for advanced devices with smaller dimensions. Therefore, this work proposes a machine learning-based device modeling algorithm to capture the complex nonlinear relationship between parameters and electrical characteristics of gate-all-around (GAA) nanowire field-effect transistors (NWFETs) from technology computer-aided design (TCAD) simulation results. This method utilizes a densely connected deep neural networks (DenseDNN), which establishes direct connections between layers in the neural networks, provides stronger feature extraction and information transmission capabilities. By incorporating cost-sensitive learning methods, the proposed model focus more on the critical data that determines device characteristics, leading to accurate prediction of key device characteristics under various parameters. Experimental results on a test dataset of 116 NWFETs demonstrate the effectiveness of this method. The DenseDNN model with cost-sensitive learning exhibits better performance than traditional deep neural networks (DNN) with various widths and depths, with a prediction error below 1.62%. Moreover, compared to TCAD simulation results, the model can speedup $10^{6}times$