{"title":"ANN-based framework for modeling process induced variation using BSIM-CMG unified model","authors":"Anant Singhal , Yogendra Machhiwar , Shashank Kumar , Girish Pahwa , Harshit Agarwal","doi":"10.1016/j.sse.2024.108988","DOIUrl":null,"url":null,"abstract":"<div><p>In this work, we present a machine-learning augmented compact modeling framework for modeling process induced variations in advanced semiconductor devices. The framework employs BSIM-CMG unified compact model at the core and can be used for any advanced devices like GAA nanosheets and nanowires, FinFETs etc. We have validated the model with extensive numerical simulations and experimental data such as <span><math><mrow><mn>14</mn><mspace></mspace><mi>nm</mi></mrow></math></span> technology FinFET and <span><math><mrow><mn>24</mn><mspace></mspace><mi>nm</mi></mrow></math></span> technology Nanowire. Our results show excellent accuracy in modeling variability in key electrical parameters of the device including off-current (<em>I</em><sub>off</sub>), on-current (<em>I</em><sub>on</sub>), threshold voltage (<em>V</em><sub>th</sub>), subthreshold swing (<em>SS</em>) etc. We observe that the overall accuracy of the ML-based framework strongly depends on the nature and physical behavior of the core model used for modeling the nominal device.</p></div>","PeriodicalId":21909,"journal":{"name":"Solid-state Electronics","volume":"220 ","pages":"Article 108988"},"PeriodicalIF":1.4000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Solid-state Electronics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038110124001370","RegionNum":4,"RegionCategory":"物理与天体物理","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
In this work, we present a machine-learning augmented compact modeling framework for modeling process induced variations in advanced semiconductor devices. The framework employs BSIM-CMG unified compact model at the core and can be used for any advanced devices like GAA nanosheets and nanowires, FinFETs etc. We have validated the model with extensive numerical simulations and experimental data such as technology FinFET and technology Nanowire. Our results show excellent accuracy in modeling variability in key electrical parameters of the device including off-current (Ioff), on-current (Ion), threshold voltage (Vth), subthreshold swing (SS) etc. We observe that the overall accuracy of the ML-based framework strongly depends on the nature and physical behavior of the core model used for modeling the nominal device.
期刊介绍:
It is the aim of this journal to bring together in one publication outstanding papers reporting new and original work in the following areas: (1) applications of solid-state physics and technology to electronics and optoelectronics, including theory and device design; (2) optical, electrical, morphological characterization techniques and parameter extraction of devices; (3) fabrication of semiconductor devices, and also device-related materials growth, measurement and evaluation; (4) the physics and modeling of submicron and nanoscale microelectronic and optoelectronic devices, including processing, measurement, and performance evaluation; (5) applications of numerical methods to the modeling and simulation of solid-state devices and processes; and (6) nanoscale electronic and optoelectronic devices, photovoltaics, sensors, and MEMS based on semiconductor and alternative electronic materials; (7) synthesis and electrooptical properties of materials for novel devices.