Control and Monitoring of Microstructural Defects in Thin Film Deposition

薄膜沉积中微观结构缺陷的控制和监测

• 批准号: 0652131
• 负责人: Panagiotis Christofides
• 金额: $ 37.21万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-15 至 2012-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0652131&HistoricalAwards=false
• 关键词: Control; Monitoring; Microstructural; Defects; Thin

PI: Panagiotis Christofides Institution: UCLAProposal Number: 0652131Title: Control and Monitoring of Microstructural Defects in Thin Film DepositionIntellectual merit: Microelectronic devices are fabricated through a series of processing steps including numerous thin film deposition processes. Owing to current trends towards decreased device dimensions, thin film properties, such as surface roughness and amount of microstructural defects have recently emerged as important film quality variables which strongly influence the overall electrical and mechanical properties of microelectronic devices. While significant research work has been recently done in achieving precise regulation of thin film surface roughness during deposition, at this point, the problem of controlling thin film microstructural defects (which can be naturally thought of as the problem of minimizing thin film porosity) despite its importance have received little attention. Thin film internal microstructure strongly influences film electrical properties since the vacancies in the microscopic structure of a thin film provide free sites for undesired electrical static charge, high leakage current and longer latency, thereby lowering transistor operating speed. For example, in the case of gate dielectrics, it is important to reduce thin film porosity as much as possible and prohibit the development of holes close to the interface. Motivated by these considerations, the objective of this research is to develop a systematic framework for the design of feedback control systems for real-time control of thin film microstructural defects using an integration of multiscale (i.e., coupled macroscopic/microscopic) models and measurements. Since thin film microstructure is determined by microscopic processes like atom adsorption, desorption and migration, a key element of the research is the introduction of a new method for the construction of stochastic dynamic models, which are suitable for controller design and real-time controller implementation, using data from multiscale process models that predict the effect of controllable process variables on thin film porosity. On the basis of these stochastic models, nonlinear and predictive control theory will be developed and used to produce practically-implementable, feedback control systems that enforce the desired stability, performance and robustness specifications in the closed-loop system. Specifically, the research will focus on the following projects:1. Multiscale modeling of thin film growth capturing the relationship between controllable (macroscopic) process variables and thin film porosity.2. Construction of stochastic dynamic models that describe the effect of controllable process variables on the evolution of thin film porosity using multiscale process model data.3. Design of estimation and feedback control systems for real-time thin film porosity minimization using the stochastic dynamic models and process measurements.4. Design of fault-detection filters for assessing actuator/sensor/controller abnormal behavior and controller reconfiguration strategies for dealing with abnormal process events.5. Applications to thin film growth processes using detailed multiscale models and realistic porosity specifications.The research will be carried out in close collaboration with engineers and scientists at Intel Corporation.Broader impact: These control methods are expected to improve the operation and performance of semiconductor manufacturing processes, increase process yield and reliability, and minimize the negative economic impact of failures on overall process operation. The research will address the design of feedback control and estimation systems accounting explicitly for multiscale process behavior and the occurrence of actuator/sensor/controller faults, as well as addressing the integration of fault-detection and control strategies. The incorporation of research results into education and the publication of a book will benefit educators and students through the development and offering of advanced-level courses in process control and multiscale modeling. The development of software, short courses and workshops and the collaboration with Intel Corporation will be the means for transferring the results of this research to the industrial sector. Moreover, the research will benefit from and contribute to a number of educational initiatives and innovations in the UCLA campus in the area of systems, dynamics and control.

主要研究者：Panagiotis Christofides机构：UCLA提案编号：0652131职务：薄膜沉积中微结构缺陷的控制和监测智能化优点：微电子器件是通过一系列工艺步骤制造的，包括许多薄膜沉积工艺。由于目前的趋势，减少设备的尺寸，薄膜性能，如表面粗糙度和微结构缺陷的数量，最近出现了重要的薄膜质量变量，强烈影响微电子器件的整体电气和机械性能。虽然最近在实现沉积期间薄膜表面粗糙度的精确调节方面已经进行了大量的研究工作，但是在这一点上，控制薄膜微观结构缺陷的问题（其可以自然地被认为是使薄膜孔隙率最小化的问题）尽管其重要性却很少受到关注。薄膜内部微结构强烈地影响薄膜电性能，因为薄膜的微观结构中的空位为不期望的静电电荷、高漏电流和较长的等待时间提供自由位置，从而降低晶体管操作速度。例如，在栅极光刻的情况下，重要的是尽可能地减少薄膜孔隙率，并禁止靠近界面的空穴的发展。出于这些考虑，本研究的目标是开发一个系统的框架，用于设计反馈控制系统，用于使用多尺度（即，耦合的宏观/微观）模型和测量。由于薄膜的微观结构是由微观过程，如原子吸附，解吸和迁移，研究的一个关键要素是引入一种新的方法，用于随机动态模型的建设，这是适合控制器的设计和实时控制器的实施，使用数据从多尺度过程模型，预测可控的工艺变量对薄膜孔隙率的影响。在这些随机模型的基础上，将开发非线性和预测控制理论，并用于产生实际可实现的反馈控制系统，该系统在闭环系统中执行所需的稳定性，性能和鲁棒性规范。具体而言，研究将集中在以下项目：1。薄膜生长的多尺度建模，捕捉可控（宏观）过程变量和薄膜孔隙率之间的关系.使用多尺度过程模型数据构建描述可控过程变量对薄膜孔隙率演化的影响的随机动态模型.利用随机动态模型和过程测量设计实时薄膜孔隙率最小化的估计和反馈控制系统.设计了用于评估执行器/传感器/控制器异常行为的故障检测滤波器和用于处理异常过程事件的控制器重构策略.该研究将与英特尔公司的工程师和科学家密切合作进行。更广泛的影响：这些控制方法有望改善半导体制造工艺的操作和性能，提高工艺产量和可靠性，并将故障对整个工艺操作的负面经济影响降到最低。该研究将解决反馈控制和估计系统的设计，明确多尺度过程行为和执行器/传感器/控制器故障的发生，以及解决故障检测和控制策略的集成。将研究成果纳入教育和出版一本书，将有利于教育工作者和学生通过开发和提供先进的课程，在过程控制和多尺度建模。软件开发、短期课程和讲习班以及与英特尔公司的合作将是将这项研究成果转移到工业部门的手段。此外，该研究将受益于并有助于加州大学洛杉矶分校校园在系统，动力学和控制领域的一些教育举措和创新。