Machine learning-based volume diagnosis

In this paper, a novel diagnosis method is proposed. The proposed technique uses machine learning techniques instead of traditional cause-effect and/or effect-cause analysis. The proposed technique has several advantages over traditional diagnosis methods, especially for volume diagnosis. In the proposed method, since the time consuming diagnosis process is reduced to merely evaluating several decision functions, run time complexity is much lower than traditional diagnosis methods. The proposed technique can provide not only high resolution diagnosis but also statistical data by classifying defective chips according to locations of their defects. Even with highly compressed output responses, the proposed diagnosis technique can correctly locate defect locations for most defective chips. The proposed technique correctly located defects for more than 90% (86%) defective chips at 50times (100times) output compaction. Run time for diagnosing a single simulated defect chip was only tens of milli-seconds.