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MODELING THE PIEZOELECTRIC PROPERTIES OF NANOMATERIALS IN ATOMIC FORCE MICROSCOPY 原子力显微镜下纳米材料压电特性的模拟
Mathematical modeling in materials science of electronic component Pub Date : 2021-10-27 DOI: 10.29003/m2485.mmmsec-2021/107-108
I. Bdikin
{"title":"MODELING THE PIEZOELECTRIC PROPERTIES OF NANOMATERIALS IN ATOMIC FORCE MICROSCOPY","authors":"I. Bdikin","doi":"10.29003/m2485.mmmsec-2021/107-108","DOIUrl":"https://doi.org/10.29003/m2485.mmmsec-2021/107-108","url":null,"abstract":"","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129820384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DATA STORAGE FOR PARALLEL COMPUTING IN INDIVIDUAL SOFTWARE ENVIRONMENTS WHEN SOLVING MATERIALS SCIENCE PROBLEMS 解决材料科学问题时，单个软件环境中并行计算的数据存储
Mathematical modeling in materials science of electronic component Pub Date : 2021-10-27 DOI: 10.29003/m2457.mmmsec-2021/11-15
K. Volovich, S. Denisov
{"title":"DATA STORAGE FOR PARALLEL COMPUTING IN INDIVIDUAL SOFTWARE ENVIRONMENTS WHEN SOLVING MATERIALS SCIENCE PROBLEMS","authors":"K. Volovich, S. Denisov","doi":"10.29003/m2457.mmmsec-2021/11-15","DOIUrl":"https://doi.org/10.29003/m2457.mmmsec-2021/11-15","url":null,"abstract":"The paper discusses methods of data storage when performing parallel computations in a multicomputer high-performance computing complex in virtual software environments. Approaches to building a data storage system using software systems designed to solve problems of materials science are proposed.","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125574479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PREDICTION OF THE BAND GAP OF CRYSTAL MATERIALS USING GRAPH NEURAL NETWORK 用图神经网络预测晶体材料的带隙
Mathematical modeling in materials science of electronic component Pub Date : 2021-10-27 DOI: 10.29003/m2477.mmmsec-2021/81-83
I. Rubtsov, Kirill Karpov, A. Mitrofanov
{"title":"PREDICTION OF THE BAND GAP OF CRYSTAL MATERIALS USING GRAPH NEURAL NETWORK","authors":"I. Rubtsov, Kirill Karpov, A. Mitrofanov","doi":"10.29003/m2477.mmmsec-2021/81-83","DOIUrl":"https://doi.org/10.29003/m2477.mmmsec-2021/81-83","url":null,"abstract":"In this paper, graph convolutional neural network is modeled to predict band gap from the crystal structure using the experimental base.","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128830559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MODERN PROBLEMS OF CREATING NEW MATERIALS WITH GIVEN PROPERTIES USING RESEARCH INFRASTRUCTURE 利用研究基础设施创造具有给定特性的新材料的现代问题
Mathematical modeling in materials science of electronic component Pub Date : 2021-10-27 DOI: 10.29003/m2458.mmmsec-2021/15-22
A. Zatsarinnyy, K. Abgaryan
{"title":"MODERN PROBLEMS OF CREATING NEW MATERIALS WITH GIVEN PROPERTIES USING RESEARCH INFRASTRUCTURE","authors":"A. Zatsarinnyy, K. Abgaryan","doi":"10.29003/m2458.mmmsec-2021/15-22","DOIUrl":"https://doi.org/10.29003/m2458.mmmsec-2021/15-22","url":null,"abstract":"The report examines topical problems associated with the creation of new materials with desired properties – the most important factors in the successful development of the digital transformation of society. The need to develop a high-performance user infrastructure for information support of multiscale computer modeling methods and their application in the field of creating a domestic electronic component base is noted","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"60 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130869238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
STOCHASTIC MODEL OF THE SPIKING NEUROMORPHIC NETWORK 脉冲神经形态网络的随机模型
Mathematical modeling in materials science of electronic component Pub Date : 2021-10-27 DOI: 10.29003/m2492.mmmsec-2021/133-136
A. Morozov, K. Abgaryan, D. Reviznikov
{"title":"STOCHASTIC MODEL OF THE SPIKING NEUROMORPHIC NETWORK","authors":"A. Morozov, K. Abgaryan, D. Reviznikov","doi":"10.29003/m2492.mmmsec-2021/133-136","DOIUrl":"https://doi.org/10.29003/m2492.mmmsec-2021/133-136","url":null,"abstract":"The work is devoted to the simulation of an analog neural network based on memristive elements, taking into account the stochastic dynamics of their functioning.","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"40 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133846762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PULSE GROWTH OF THE GAAS NANOWIRES 气体纳米线的脉冲生长
Mathematical modeling in materials science of electronic component Pub Date : 2021-10-27 DOI: 10.29003/m2474.mmmsec-2021/72-75
A. Nastovjak, D. Shterental, N. Shwartz
{"title":"PULSE GROWTH OF THE GAAS NANOWIRES","authors":"A. Nastovjak, D. Shterental, N. Shwartz","doi":"10.29003/m2474.mmmsec-2021/72-75","DOIUrl":"https://doi.org/10.29003/m2474.mmmsec-2021/72-75","url":null,"abstract":"The results of the simulation of the GaAs nanowire self-catalyzed growth via vapor-liquid-solid mechanism using various pulse modes are presented in this work.","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115006945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
OPTIMIZATION OF THE INPUT DATA VECTOR TO IMPROVE THE NEURAL NETWORK TRAINING FOR OPC 优化输入数据向量，改进opc神经网络训练
Mathematical modeling in materials science of electronic component Pub Date : 2021-10-27 DOI: 10.29003/m2493.mmmsec-2021/137-140
G. Teplov, Almira Galeeva, A. Kuzovkov
{"title":"OPTIMIZATION OF THE INPUT DATA VECTOR TO IMPROVE THE NEURAL NETWORK TRAINING FOR OPC","authors":"G. Teplov, Almira Galeeva, A. Kuzovkov","doi":"10.29003/m2493.mmmsec-2021/137-140","DOIUrl":"https://doi.org/10.29003/m2493.mmmsec-2021/137-140","url":null,"abstract":"This work explored the impact of input data structure to improve the neural network training. The impact of two variants of the input data vector on the training accuracy of the network was studied. The first version of the input vector included the intensity of the exposure radiation map. The second version of the input vector included the intensity of the exposure radiation map and IC topology.","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116904496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
APPLICATION OF NEURAL NETWORK AUTO ENCODERS OF UNET TYPE FOR INVERSE PHOTOLITOGRAPHY TASKS unet型神经网络自动编码器在逆照相任务中的应用
Mathematical modeling in materials science of electronic component Pub Date : 2021-10-27 DOI: 10.29003/m2459.mmmsec-2021/22-25
Vladimir Keremet, Yakov M. Karandashev, A. Kuzovkov, Georgy Teplov
{"title":"APPLICATION OF NEURAL NETWORK AUTO ENCODERS OF UNET TYPE FOR INVERSE PHOTOLITOGRAPHY TASKS","authors":"Vladimir Keremet, Yakov M. Karandashev, A. Kuzovkov, Georgy Teplov","doi":"10.29003/m2459.mmmsec-2021/22-25","DOIUrl":"https://doi.org/10.29003/m2459.mmmsec-2021/22-25","url":null,"abstract":"The paper discusses the issue of the applicability of neural networks to the problems of designing microelectronics. The integration of neural network modules into the elements of specialized EDA systems can significantly speed up the modeling processes at different stages of design. The application of a multilayer convolutional architecture of a neural network of the UNET type to the problem of direct and inverse computational photolithography is considered. Using this neural network approach, we were able to speed up the computation of a photo mask for a 90nm process technology by two orders of magnitude and achieve simulation accuracy that surpasses standard inverse photolithography (ILT) methods.","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114406437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MULTI-SCALE MODELING AND METHODS OF DATA SCIENCE IN PROBLEMS OF MICROELECTRONICS 微电子问题中的多尺度建模与数据科学方法
Mathematical modeling in materials science of electronic component Pub Date : 2021-10-27 DOI: 10.29003/m2467.mmmsec-2021/50-53
K. Abgaryan
{"title":"MULTI-SCALE MODELING AND METHODS OF DATA SCIENCE IN PROBLEMS OF MICROELECTRONICS","authors":"K. Abgaryan","doi":"10.29003/m2467.mmmsec-2021/50-53","DOIUrl":"https://doi.org/10.29003/m2467.mmmsec-2021/50-53","url":null,"abstract":"The report is devoted to the problem of integrating multiscale modeling and data analysis methods to create predictive models based on approaches based on theoretical physical and mathematical modeling using the mathematical apparatus of data analysis.","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124018125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MACHINE LEARNING THE BAND GAP OF METAL-ORGANIC FRAMEWORKS 机器学习金属有机框架的带隙
Mathematical modeling in materials science of electronic component Pub Date : 2021-10-27 DOI: 10.29003/m2478.mmmsec-2021/84-86
S. Savelyev, K. Mitrofanov, Artem Karpov
{"title":"MACHINE LEARNING THE BAND GAP OF METAL-ORGANIC FRAMEWORKS","authors":"S. Savelyev, K. Mitrofanov, Artem Karpov","doi":"10.29003/m2478.mmmsec-2021/84-86","DOIUrl":"https://doi.org/10.29003/m2478.mmmsec-2021/84-86","url":null,"abstract":"This work presents building and study of physico-chemically interpretable machine learning models fit for metal-organic frameworks’ band gap prediction.","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124103393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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