<title>Accurate prediction of kill ratios based on KLA defect inspection and critical area analysis</title>

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In-line defect inspection is of crucial importance in controlling the quality of VLSI manufacturing processes. Wafer inspection tools such as KLA 21XX coupled with the review/classification stations, such as KLA25XX can be extremely effective in detecting problems in the processed batches of wafers. However, there is a need to properly allocate these machines among several process steps (levels) to maximize their utilization. Moreover, results of this in- line inspection such as defect distributions, both spatial and size, could be used to predict the yield impact of the detected defects. Up to now, the so-called 'kill ratios' were used to indicate the yield impact of defects per layer (e.g., polysilicon, metal1, metal2). The typical accuracy of 'kill ratio' based prediction was around 25%. In this paper, we demonstrate the methodology of yield impact prediction based on critical area concept. Critical area is extracted from the actual product layout by a software tool called MAPEX developed at CMU. The information about defect densities and size distributions is obtained from KLA inspection equipment. A prototype of a software system was developed that combines the information about the critical area with the defect size distributions, declustering of defect data into a yield model. This methodology and software system were verified on the experimental data from AMD Austin fabrication facility. The 'yield impact' prediction (equivalent to 'kill ratio' prediction) was performed for 7 different products ranging from SRAM to microprocessor to telecommunication products. The data was collected from more than 2000 wafers. The accuracy of our prediction was verified by comparing our functional yield estimate to the actual yield values from the probe testing measurements. We obtained an accuracy of 1% for the defects found by KLA in h polysilicon layer for dies affected by single defects only. Moreover, we were able to normalize the defect inspection data for the entire product family. This was possible due to the availability of information about critical area for each of the product. Hence, we were able to demonstrate the advantage of this methodology of determining the 'kill ratios' for the defect inspection data.