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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >An adaptive diagnosis system for copper wire bonding process control and quality assessment in integrated circuit assembly
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An adaptive diagnosis system for copper wire bonding process control and quality assessment in integrated circuit assembly

机译：集成电路组件中铜线焊接工艺控制和质量评估的自适应诊断系统

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摘要

Copper (Cu) wire has become an alternative material for wire bonding in many microelectronic applications due to the high appreciation in the price of gold. However, the Cu wire bonding process is relatively new to integrated circuit (IC) assembly and must be appropriately controlled to save on manufacturing costs without encountering reliability problems or losing quality. This study proposes an adaptive diagnosis system for the control and quality assessment of the Cu wire bonding process using grey relational analysis (GRA) and a neurofuzzy technique. A fractional factorial experimental design is first utilised to collect structured data, and the results are analysed through an integrated GRA and entropy measurement method to convert the multiple quality characteristics of Cu wire bonding into a synthetic performance index. Next, the neurofuzzy datalearning technique is used to establish the essential knowledge bases. The in-process-quality-control (IPQC) data are then clustered and utilised to fine-tune the membership functions and adjust the weights of fuzzy rules through neurofuzzy data-learning. Finally, customised programming codes are generated for fuzzy rule retrieval and for a graphical user interface design to link users and the process diagnostic and quality assessment knowledge bases. The proposed diagnosis system is evaluated using real-world production data collected from an electronics manufacturing service (EMS) provider of IC packaging to verify its prediction accuracy and applicability.
机译：由于金价的高涨，铜（Cu）线已成为许多微电子应用中用于引线键合的替代材料。但是，铜线键合工艺对于集成电路（IC）组件而言相对较新，必须进行适当控制以节省制造成本，而不会遇到可靠性问题或质量下降。这项研究提出了一种自适应诊断系统，用于使用灰色关联分析（GRA）和神经模糊技术对铜线键合过程进行控制和质量评估。首先使用分数阶乘实验设计来收集结构化数据，然后通过集成的GRA和熵测量方法对结果进行分析，以将Cu引线键合的多个质量特征转换为综合性能指标。接下来，使用神经模糊数据学习技术来建立必要的知识库。然后，对过程中质量控制（IPQC）数据进行聚类，并通过神经模糊数据学习将其用于微调隶属函数并调整模糊规则的权重。最后，生成用于模糊规则检索和图形用户界面设计的定制编程代码，以链接用户以及过程诊断和质量评估知识库。建议的诊断系统使用从IC封装的电子制造服务（EMS）提供商那里收集的实际生产数据进行评估，以验证其预测准确性和适用性。

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