Overview and Prospect of Virtual Metrology Technology for Manufacturing Processes
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摘要:
“零缺陷制造”作为工业4.0的拓展，致力于大幅提升产品良率并最终实现产品零瑕疵。目前，工业过程通常采取物理检测方式对产品进行质量检验，属于离线破坏性试验且检测成本高昂，检测结果无法及时指导生产。虚拟量测通过对生产过程数据进行监控、对产品品质或工艺进行预判，能够将传统离线且具延迟特性的品质抽检改成线上且即时的品质全检。本文首先沿时间线对虚拟量测的发展历程进行了综述；随后介绍了虚拟量测的研究现状和典型应用场景，特别是半导体制造领域；接着汇总了常见的虚拟量测技术方法及其所解决的实际工程问题，比如数据预处理方法、预测建模方法和系统功能设计；最后，对制造过程虚拟量测问题进行了展望，提出了一种集数据预处理与可视化、虚拟量测和质量追溯为一体的工业制造过程智能管理体系。
Abstract:As an outgrowth of Industry 4.0, "zero-defect manufacturing" is dedicated to dramatically improving product yield and ultimately achieving zero-defect products. Presently, the manufacturing process mainly adopts the physical inspection method for product quality inspection, which is an off-line test with high detection cost and delayed guide. By monitoring production process data and predicting product quality or process, virtual metrology (VM) may transform the traditional off-line and delayed quality sampling into online and real-time quality full inspection. Firstly, we summarize the development of VM over time. Then the research status and typical application scenarios of VM, especially in semiconductor manufacturing, are introduced. Subsequently, the common VM techniques and practical engineering problems are outlined, such as data preprocessing, predictive modeling methods and system function design. Finally, we prospect the manufacturing process VM problem, and propose a manufacturing process intelligent management system, which integrates data preprocessing and visualization, VM and quality tracing.
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Keywords:
- zero-defect manufacturing /
- product yield /
- virtual metrology
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图 7 提出的数智驱动AVM系统框架
Figure 7. The proposed digital intelligence driven AVM system framework
表 1 VM预测算法
Table 1 VM prediction algorithm
方法 算法 优点 限制 线性模型 多元线性回归(MLR) 复杂度低，计算效率高 线性，稳定性较差 偏最小二乘(PLS) 可解释，计算效率高，特征提取 对离群值敏感，线性，可解释性较差 Lasso 计算效率高，特征选择，可解释 线性，高相关变量性能差，若描述元数量超过观测数量则性能低 神经网络 多层感知器(MLP) 非线性，从训练集中的特征子集生成新特征 计算要求高，数据集需求大，黑盒 卷积神经网络(CNN) 非线性，特征提取，鲁棒性强 计算要求高，数据集需求大，黑盒 递归神经网络(RNN) 非线性，有时间记忆 计算要求高，数据集需求大，黑盒 贝叶斯神经网络(BNN) 非线性，因果性，先验知识 计算要求高，数据集需求大，黑盒 核方法 高斯过程回归(GPR) 非线性，计算效率高，内置不确定性量化 计算要求高，可扩展性差，难以调优，高斯性 支持向量回归(SVR) 非线性，效率高，不易过拟合 可扩展性差，难以调优 
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