Control and Monitoring of Microstructural Defects in Thin Film Deposition

PI: Panagiotis Christofides机构：加州大学洛杉矶分校提案号：0652131标题：薄膜沉积中微结构缺陷的控制和监测知识优点：微电子器件是通过一系列加工步骤制造的，包括许多薄膜沉积过程。由于当前器件尺寸减小的趋势，薄膜性能，如表面粗糙度和微结构缺陷的数量最近成为重要的薄膜质量变量，它们强烈影响微电子器件的整体电气和机械性能。虽然在沉积过程中实现薄膜表面粗糙度的精确调节方面已经做了大量的研究工作，但在这一点上，控制薄膜微结构缺陷的问题（自然可以认为是最小化薄膜孔隙率的问题）尽管很重要，但却很少受到关注。薄膜内部微观结构对薄膜电学性能的影响很大，因为薄膜微观结构中的空位为不需要的静电、高泄漏电流和更长的延迟提供了自由的场所，从而降低了晶体管的工作速度。例如，在栅极电介质的情况下，重要的是尽可能减少薄膜孔隙率，并禁止在靠近界面的地方形成孔。出于这些考虑，本研究的目标是开发一个系统框架，用于设计反馈控制系统，利用多尺度（即宏观/微观耦合）模型和测量的集成来实时控制薄膜微结构缺陷。由于薄膜微观结构是由原子吸附、解吸和迁移等微观过程决定的，因此本研究的一个关键要素是引入一种新的方法来构建随机动态模型，该模型适用于控制器设计和实时控制器实现，利用多尺度过程模型的数据来预测可控过程变量对薄膜孔隙率的影响。在这些随机模型的基础上，非线性和预测控制理论将被开发并用于生产实际可实施的反馈控制系统，这些系统在闭环系统中强制执行所需的稳定性，性能和鲁棒性规范。具体而言，研究将集中在以下几个项目上：1。捕获可控（宏观）过程变量与薄膜孔隙率之间关系的薄膜生长的多尺度建模。2 .利用多尺度过程模型数据建立描述可控过程变量对薄膜孔隙度演化影响的随机动力学模型。基于随机动态模型和过程测量的实时薄膜孔隙率最小化估计和反馈控制系统的设计。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设计以及处理异常过程事件的控制器重构策略。应用到薄膜生长过程中使用详细的多尺度模型和现实的孔隙率规格。这项研究将与英特尔公司的工程师和科学家密切合作。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，提高工艺良率和可靠性，并最大限度地减少故障对整个工艺运行的负面经济影响。该研究将解决反馈控制和估计系统的设计，明确地考虑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通过开发和提供过程控制和多尺度建模的高级课程，使教育工作者和学生受益。软件的开发、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，这项研究将受益于加州大学洛杉矶分校在系统、动力学和控制领域的一些教育倡议和创新，并为之做出贡献。

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.