A Method to Perform Soft Material Spectroscopy of a Defect
International Conference Industrial Technology and Management(2021)
Abstract
Material spectroscopy (MS) is used to identify elemental composition of micro particles. Energy dispersive X-Ray spectroscopy (EDX or EDS) is one such method. EDX analysis of defects found during wafer inspection aids in performing their root cause analysis (RCA). However, due to large processing time of EDX, it is applied very judiciously on a few chosen defects only. A wafer can typically contain ~100s of defects. The defect coverage of EDX is ~1% [1] thereby resulting in considerable gap in proper diagnosis and RCA. To overcome this issue, we demonstrate a soft method to perform MS of defects. The method predicts accurate elemental compositions of defect and background (~80%F1) when compared with EDX predictions on the same defect. The method is fast and could increase defect coverage for MS to ~100%• This can significantly improve RCA and thus help in Yield Enhancement (YE). Computing exact YE is complex as it involves many hidden and un-trackable factors. We perform theoretical high level modelling of more tangible factors i.e. profitability per month of Fab which is directly proportional to YE and theoretically show 14.6% improvement using our soft MS method.
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Key words
virtual EDX,yield enhancement,spectroscopy,material composition,deep learning
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