A. Mavromaras, D. Rigby, W. Wolf, M. Christensen, M. Halls, C. Freeman, P. Saxe, E. Wimmer
{"title":"计算材料工程:微电子材料的力学、热学和电学性质的原子尺度预测能力","authors":"A. Mavromaras, D. Rigby, W. Wolf, M. Christensen, M. Halls, C. Freeman, P. Saxe, E. Wimmer","doi":"10.1109/ESIME.2010.5464604","DOIUrl":null,"url":null,"abstract":"Atomic-scale computational materials engineering offers an exciting complement to experimental observations, revealing critical materials property data, and providing understanding which can form the basis for innovation. This contribution reviews the current state of atomic-scale simulations and their capabilities to predict mechanical, thermal, and electric properties of microelectronics materials. Specific examples are the elastic moduli of compounds such as aluminum oxide, the strength of an aluminum/silicon nitride interface, the first-principles prediction of coefficients of thermal expansion of bulk aluminum and silicon nitride, thermal conductivity of polyethylene, the prediction of the diffusion coefficient of hydrogen in metallic nickel, the calculation of dielectric properties of zinc oxide and optical properties of silicon carbide. The final example illustrates the control of the work function in the HfO2/TiN interface of a CMOS gate stack. For an increasing number of materials properties, computed values possess accuracies similar to measured data. Such accuracy has become possible due to advances in theoretical approaches and numerical algorithms combined with the astounding increase in compute power.","PeriodicalId":152004,"journal":{"name":"2010 11th International Thermal, Mechanical & Multi-Physics Simulation, and Experiments in Microelectronics and Microsystems (EuroSimE)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Computational materials engineering: Capabilities of atomic-scale prediction of mechanical, thermal, and electrical properties of microelectronic materials\",\"authors\":\"A. Mavromaras, D. Rigby, W. Wolf, M. Christensen, M. Halls, C. Freeman, P. Saxe, E. Wimmer\",\"doi\":\"10.1109/ESIME.2010.5464604\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Atomic-scale computational materials engineering offers an exciting complement to experimental observations, revealing critical materials property data, and providing understanding which can form the basis for innovation. This contribution reviews the current state of atomic-scale simulations and their capabilities to predict mechanical, thermal, and electric properties of microelectronics materials. Specific examples are the elastic moduli of compounds such as aluminum oxide, the strength of an aluminum/silicon nitride interface, the first-principles prediction of coefficients of thermal expansion of bulk aluminum and silicon nitride, thermal conductivity of polyethylene, the prediction of the diffusion coefficient of hydrogen in metallic nickel, the calculation of dielectric properties of zinc oxide and optical properties of silicon carbide. The final example illustrates the control of the work function in the HfO2/TiN interface of a CMOS gate stack. For an increasing number of materials properties, computed values possess accuracies similar to measured data. Such accuracy has become possible due to advances in theoretical approaches and numerical algorithms combined with the astounding increase in compute power.\",\"PeriodicalId\":152004,\"journal\":{\"name\":\"2010 11th International Thermal, Mechanical & Multi-Physics Simulation, and Experiments in Microelectronics and Microsystems (EuroSimE)\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 11th International Thermal, Mechanical & Multi-Physics Simulation, and Experiments in Microelectronics and Microsystems (EuroSimE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ESIME.2010.5464604\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 11th International Thermal, Mechanical & Multi-Physics Simulation, and Experiments in Microelectronics and Microsystems (EuroSimE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ESIME.2010.5464604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computational materials engineering: Capabilities of atomic-scale prediction of mechanical, thermal, and electrical properties of microelectronic materials
Atomic-scale computational materials engineering offers an exciting complement to experimental observations, revealing critical materials property data, and providing understanding which can form the basis for innovation. This contribution reviews the current state of atomic-scale simulations and their capabilities to predict mechanical, thermal, and electric properties of microelectronics materials. Specific examples are the elastic moduli of compounds such as aluminum oxide, the strength of an aluminum/silicon nitride interface, the first-principles prediction of coefficients of thermal expansion of bulk aluminum and silicon nitride, thermal conductivity of polyethylene, the prediction of the diffusion coefficient of hydrogen in metallic nickel, the calculation of dielectric properties of zinc oxide and optical properties of silicon carbide. The final example illustrates the control of the work function in the HfO2/TiN interface of a CMOS gate stack. For an increasing number of materials properties, computed values possess accuracies similar to measured data. Such accuracy has become possible due to advances in theoretical approaches and numerical algorithms combined with the astounding increase in compute power.