Monitoring the operating state of crystal growth process based on digital twin model

IF 3.3 2区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Journal of Process Control Pub Date : 2024-06-19 DOI:10.1016/j.jprocont.2024.103261
Yu-Yu Liu , Ling-Xia Mu , Ding Liu
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Abstract

The reliable assessment of the operational status during the silicon single crystal growth process is a prerequisite for ensuring system safety and improving crystal quality. However, in the actual silicon single crystal growth process, due to limitations in manpower, material resources, financial resources, and current technical methods, the establishment of monitoring models is still in its infancy. To address this issue, this paper proposes a hybrid deep belief network (HDBN) algorithm aided by the digital twin (DT) model to achieve real-time monitoring of equipment operational status. Firstly, this study constructs the DT model based on the basic principles of crystal growth, mainly to achieve high-precision simulation of the actual silicon single crystal growth process and generate abnormal data for the equipment. This operation can expand the sample set, enhance the diversity and coverage of data, and effectively solve the problem of insufficient sample size. Secondly, this study uses the variational mode decomposition (VMD) algorithm to decompose the dataset composed of obtained abnormal and normal data, and constructs sub-deep belief network (DBN) for the decomposed subsequences to capture deep feature information at different frequencies of the data. Subsequently, based on the concept of ensemble learning, the outputs of each sub-DBN network are used as inputs to construct the overall DBN network, achieving monitoring of the equipment operational status. Through the combination of VMD decomposition and DBN networks, this algorithm can better capture the frequency characteristics and time-domain features of the signal, enhancing monitoring accuracy. Experimental results show that this algorithm can accurately identify abnormal equipment states, effectively improve monitoring performance, and is of significant importance for the optimization and control of the semiconductor-grade silicon single crystal growth process, contributing to increased production efficiency and product quality.

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基于数字孪生模型监控晶体生长过程的运行状态
对硅单晶生长过程中的运行状态进行可靠评估,是确保系统安全和提高晶体质量的前提。然而,在实际的硅单晶生长过程中,由于人力、物力、财力以及现有技术手段的限制,监测模型的建立仍处于起步阶段。针对这一问题,本文提出了一种以数字孪生(DT)模型为辅助的混合深度信念网络(HDBN)算法,以实现对设备运行状态的实时监控。首先,本研究基于晶体生长的基本原理构建了 DT 模型,主要实现对实际硅单晶生长过程的高精度模拟,并生成设备的异常数据。这一操作可以扩大样本集,增强数据的多样性和覆盖面,有效解决样本量不足的问题。其次,本研究利用变异模态分解(VMD)算法对获取的异常数据和正常数据组成的数据集进行分解,并对分解后的子序列构建子深度信念网络(DBN),以捕捉数据不同频率的深度特征信息。随后,基于集合学习的概念,将各子 DBN 网络的输出作为输入,构建整体 DBN 网络,实现对设备运行状态的监测。通过 VMD 分解与 DBN 网络的结合,该算法能更好地捕捉信号的频率特性和时域特征,提高监测精度。实验结果表明,该算法能准确识别设备异常状态,有效提高监测性能,对半导体级硅单晶生长过程的优化和控制具有重要意义,有助于提高生产效率和产品质量。
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来源期刊
Journal of Process Control
Journal of Process Control 工程技术-工程:化工
CiteScore
7.00
自引率
11.90%
发文量
159
审稿时长
74 days
期刊介绍: This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others. Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques. Topics covered include: • Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.
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