| Summary: | 碩士 === 國立交通大學 === 理學院應用科技學程 === 100 === Wafer probing is a critical process employed to measure the yield of wafer fabrication. The major object of wafer probing is to find the defect dice on the wafer. It inputs electrical current and signals through the probe needles which contact the pad of each dice and receives the outputs to determine whether or not the dice are good.
However, the probing result could be affected by the stability of tester, prober, probe card or the setting actions of operators. Overkill situations happen if good dice are misjudged as bad dice caused by one or more of the above factors causing an abnormal yield and requiring re-probe actions which diminish production performance and the trust of customers.
In this study, we talked to the specialists of this industry in order to build some overkill-related detection methods. The aim was to implement these detection methods based on the real wafer probing data from one of Taiwan’s testing facilities. We used classification technologies of data mining on those results generated by these detection methods to determine the correctness of these methods, to ascertain if they could be implemented in the data analysis system as a real-time alarm to handle the overkill issue.
The root causes of overkill situations are not easy to find. They include: stability of tester, usage of probe card, setting actions by operators or a combination of these factors. We used association rule technology of data mining to find the possible causes of overkill situation, to serve as reference for improving actions.
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