摘要
在全球科技竞争日益激烈的背景下,传统芯片故障诊断模式依赖人工经验,存在准确性欠佳、耗时费力及成本高昂等问题,已难以满足现代半导体产业对高效、精准诊断的需求。本研究聚焦于深度学习技术在芯片故障诊断中的应用,旨在推动芯片故障诊断从“被动响应”向“主动预防”转型。通过理论综述与文献回顾,本研究发现深度学习凭借其强大的数据处理与模式识别能力,在设备故障预测、质量缺陷分类及生产异常检测等领域展现出巨大潜力。从管理框架与技术路径两个维度着手,构建了基于“目标-过程-资源”三维模型的优化路径,并提出了混合深度学习模型(CNN+LSTM+Attention机制)用于芯片故障诊断。结果表明,深度学习技术在芯片故障诊断中显著优于传统方法。具体而言,深度学习诊断在人力成本、设备成本和停机成本上均表现出明显优势,故障预测准确率随数据规模扩大而显著提升,关键管理指标如故障预警提前时间、维修响应速度和产品报废率均得到显著改善。通过案例分析,进一步验证了深度学习技术在晶圆缺陷预测优化和设备故障根因分析中的有效性。本研究不仅为芯片制造企业提供了数据驱动的故障诊断管理优化框架,还验证了深度学习在复杂制造系统中的管理适配性,拓展了运营管理理论的边界。同时,研究也指出了深度学习驱动的故障诊断实践面临的挑战,如数据孤岛问题、模型可解释性差及组织变革阻力大等,并提出了相应的实践启示与未来研究方向。
Against the backdrop of increasingly fierce global technological competition,the traditional model of chip fault diagnosis,which relies on manual experience,suffers from issues such as suboptimal accuracy,time-consuming and labor-intensive processes,and high costs.This model struggles to meet the demands of the modern semiconductor industry for efficient and precise diagnosis.This study focuses on the application of deep learning technology in chip fault diagnosis,aiming to drive the transformation of chip fault diagnosis from"passive response"to"proactive prevention".Through theoretical overview and literature review,this research reveals that deep learning,with its powerful data processing and pattern recognition capabilities,demonstrates significant potential in areas such as equipment fault prediction,quality defect classification,and production anomaly detection.From the perspectives of management framework and technical pathway,an optimization pathway based on a three-dimensional"objective-process-resource"model is constructed,and a hybrid deep learning model(combining CNN,LSTM,and the Attention mechanism)is proposed for chip fault diagnosis.The results indicate that deep learning technology significantly outperforms traditional methods in chip fault diagnosis.Specifically,deep learning-based diagnosis exhibits clear advantages in terms of labor costs,equipment costs,and downtime costs.The accuracy of fault prediction improves notably with the expansion of data scale,and key management indicators such as the lead time for fault warnings,maintenance response speed,and product scrap rate are all significantly enhanced.Through case analysis,the effectiveness of deep learning technology in optimizing wafer defect prediction and analyzing the root causes of equipment faults is further validated.This study not only provides chip manufacturing enterprises with a data-driven management optimization framework for fault diagnosis but also verifies the management adaptability of deep learning in complex manufacturing systems,thereby expanding the boundaries of operations management theory.Meanwhile,the study also points out the challenges faced in the practice of deep learning-driven fault diagnosis,such as data silos,poor model interpretability,and significant resistance to organizational change,and proposes corresponding practical insights and future research directions.
作者
李瑞媛
李万祺
Ruiyuan Li;Wanqi Li(Aerospace Science and Industry Defense Technology Research Testing Center,Beijing;Zhejiang Moganshan Institute of Geomagnetism Large-scale Scientific Facility,313200)
关键词
深度学习
芯片故障诊断
主动预防
混合深度学习模型
管理优化
Deep Learning
Chip Fault Diagnosis
Proactive Prevention
Hybrid Deep Learning Model
Management Optimization
