This survey paper offers a comprehensive review of methodologies utilizing
machine learning (ML) techniques for identifying wafer defects in semiconductor
manufacturing. Despite the growing body of research demonstrating the
effectiveness of ML in wafer defect identification, there is a noticeable
absence of comprehensive reviews on this subject. This survey attempts to fill
this void by amalgamating available literature and providing an in-depth
analysis of the advantages, limitations, and potential applications of various
ML algorithms in the realm of wafer defect detection. An innovative taxonomy of
methodologies that we present provides a detailed classification of algorithms
into more refined categories and techniques. This taxonomy follows a four-tier
structure, starting from broad methodology categories and ending with specific
sub-techniques. It aids researchers in comprehending the complex relationships
between different algorithms and their techniques. We employ a rigorous
empirical and experimental evaluation to rank these varying techniques. For the
empirical evaluation, we assess techniques based on a set of four criteria. The
experimental evaluation ranks the algorithms employing the same sub-techniques,
techniques, sub-categories, and categories. This integration of a multi-layered
taxonomy, empirical evaluations, and comparative experiments provides a
detailed and holistic understanding of ML techniques and algorithms for
identifying wafer defects. This approach guides researchers towards making more
informed decisions in their work. Additionally, the paper illuminates the
future prospects of ML techniques for wafer defect identification, underscoring
potential advancements and opportunities for further research in this field

综述了利用机器学习技术识别半导体制造中晶圆缺陷的方法学，提供了各种机器学习算法在晶圆缺陷检测中的优势、局限性和潜在应用的深入分析，通过创新的分类方法将算法细分为更具体的类别和技术，结合实证评估和实验评估提供了对机器学习技术和算法全面的理解，指导研究人员在工作中做出更明智的决策，并阐明了晶圆缺陷识别中机器学习技术的未来前景和进一步研究的机会。