  Enhancing yield is recognized as a paramount driver to reducing production
costs in semiconductor smart manufacturing. However, optimizing and ensuring
high yield rates is a highly complex and technical challenge, especially while
maintaining reliable yield diagnosis and prognosis, and this shall require
understanding all the confounding factors in a complex condition. This study
proposes a domain-specific explainable automated machine learning technique
(termed xAutoML), which autonomously self-learns the optimal models for yield
prediction, with an extent of explainability, and also provides insights on key
diagnosis factors. The xAutoML incorporates tailored problem-solving
functionalities in an auto-optimization pipeline to address the intricacies of
semiconductor yield enhancement. Firstly, to capture the key diagnosis factors,
knowledge-informed feature extraction coupled with model-agnostic key feature
selection is designed. Secondly, combined algorithm selection and
hyperparameter tuning with adaptive loss are developed to generate optimized
classifiers for better defect prediction, and adaptively evolve in response to
shifting data patterns. Moreover, a suite of explainability tools is provided
throughout the AutoML pipeline, enhancing user understanding and fostering
trust in the automated processes. The proposed xAutoML exhibits superior
performance, with domain-specific refined countermeasures, adaptive
optimization capabilities, and embedded explainability. Findings exhibit that
the proposed xAutoML is a compelling solution for semiconductor yield
improvement, defect diagnosis, and related applications.
