With the trend of even smaller workpieces and complicated processes in the semiconductor industry, yield enhancement becomes the crucial indicator of enterprise profits. However, the advancement of the manufacturing processes results in huge-and-complicated data, it becomes difficult to quickly search for the root causes that affect the yield from such big historical data. In light of this, the authors proposed the key-variable search algorithm (KSA) to resolve this problem. The KSA scheme provides users a quick-and-efficient way to identify the root causes of a yield loss. The inputs of this KSA scheme include production routes, process data, inline data, defects, and final inspection results. However, when an interaction effect exists between a key device/variable and the other device/variable, also, the impact of this effect is greater than those impacts of the original devices/variables, the original KSA scheme may not correctly find out the root causes. To remedy this insufficiency, this study develops the 'interaction-effect search algorithm (IESA).' The IESA scheme can not only identify the existence of an interaction effect but also determine the threshold of the key variable that causes this interaction effect.
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