Integration of Computation, Experiment, Simulation and Data to Predict Defect Properties in Semiconductor Thin Films

集成计算、实验、模拟和数据来预测半导体薄膜的缺陷特性

• 批准号: 1309535
• 负责人: Robert Hull
• 金额: $ 60万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1309535&HistoricalAwards=false
• 关键词: Integration; Computation; Experiment; Simulation; Data

Technical Description: This project seeks to develop the foundations for a new framework for describing the processing of complex materials systems. It is being achieved by integrating computation, experimental measurements, simulation and data sets into a predictive descriptor of kinetic evolution during materials processing. The specific focus / example is the development of predictive simulation of the structural, electronic and optical properties of dislocation networks in epitaxial films, with an ultimate goal of accelerating the development of device-quality material in such systems. The project is first extending an existing simulator developed for predicting dislocation generation in the GeSi/Si system during growth and/or thermal processing. Phylogenetic mapping methods, developed for the fields of evolutionary biology and bioinformatics, are employed to refine materials parameters and fundamental mechanistic assumptions in the simulator by optimizing the matching of experimental and simulated data sets. An initial important goal is to define a "minimum data set" necessary to adequately describe the energetic and kinetic parameters of the evolving system, and enable quantitative predictions. Next, the project is extending these methods to a new materials system, with significantly different dislocation generation mechanisms, strained III-nitride films. This enables both assessment of the generality of the simulator method, and should advance understanding of dislocation generation in the III-nitride systems. Finally, the simulator is being extended to enable prediction of optical and electronic parameters that correlate to the observed /predicted defect densities. This involves first-principle calculations of electronic properties of specific defect configurations, enabling an additional generation of simulator that correlates predicted defect densities and consequent (opto)electronic activity to observed optoelectronic and device properties of the dislocated material.Non-Technical Description: This project is developing new frameworks for enabling predictive simulation of defect generation during growth and processing of thin-film crystalline materials, and the effects of these defects upon the performance parameters of these materials. This enables accelerated device development cycle times by predicting conditions for device-quality materials synthesis ahead of full processing cycles. This ultimately contributes to the national need to accelerate the transition from new materials discoveries to manufacturable technologies. The method to be developed employs the comparison of predictions from the simulator to extensive experimental data sets to refine and "train" the simulator. It both improves the predictive capability of the simulator for the given system in which it is trained, and accelerates transfer of its application to new materials systems. The set of students working on this project are being schooled in this vision of optimizing integration of calculation, experiment and data management, thus helping to establish a workforce trained in these methods. The research team is making the resultant process simulator accessible to the research and development community and is responding to selected requests for specific updates or enhancements.This project is co-funded by the Electronic and Photonic Materials Program (EPM) and the Computational and Data driven Materials Research Program (CDMR), both in the Division of Materials Research (DMR).

技术描述：该项目旨在为描述复杂材料系统处理的新框架奠定基础。它是通过将计算、实验测量、模拟和数据集集成到材料加工过程中动力学演变的预测描述符中来实现的。具体的重点/例子是外延膜中位错网络的结构，电子和光学特性的预测模拟的发展，最终目标是加速这种系统中器件质量材料的发展。该项目首先扩展了现有的模拟器，用于预测生长和/或热处理过程中GeSi/Si系统中位错的产生。进化生物学和生物信息学领域开发的系统发育映射方法，通过优化实验和模拟数据集的匹配来细化模拟器中的材料参数和基本机械假设。最初的一个重要目标是定义一个“最小数据集”，以充分描述不断发展的系统的能量和动力学参数，并实现定量预测。接下来，该项目将这些方法扩展到一种新的材料系统，具有显着不同的位错产生机制，应变III-氮化物薄膜。这使得两个评估的模拟器方法的一般性，并应提前理解位错生成的III族氮化物系统。最后，模拟器正在扩展，使预测的光学和电子参数，相关的观察/预测的缺陷密度。这涉及特定缺陷配置的电子性质的第一原理计算，从而能够额外生成模拟器，该模拟器将预测的缺陷密度和随之发生的（光）电子活动与观察到的错位材料的光电和器件性质相关联。该项目正在开发新的框架，用于预测模拟薄膜生长和加工过程中的缺陷生成。薄膜晶体材料，以及这些缺陷对这些材料的性能参数的影响。这通过在整个加工周期之前预测器件质量材料合成的条件来加快器件开发周期。这最终有助于国家加速从新材料发现到可制造技术的过渡。开发的方法采用模拟器的预测比较广泛的实验数据集，以完善和“训练”模拟器。它既提高了模拟器对给定系统的预测能力，又加速了其应用到新材料系统的转移。参与该项目的学生正在接受优化计算，实验和数据管理整合的愿景的教育，从而帮助建立一支受过这些方法培训的员工队伍。该研究团队正在使最终的过程模拟器可供研究和开发社区使用，并对特定更新或增强的特定请求做出回应。该项目由材料研究部（DMR）的电子和光子材料计划（EMT）和计算和数据驱动材料研究计划（CDMR）共同资助。