We propose a general method for repair-yield estimation based on critical area analysis using a commercial Monte-Carlo simulator. We classify failures into several types according to the repair rules and use iterative critical area analysis for each type of failure (ICAA-ETF) to calculate the repair yield. Our proposed method makes it possible to accurately estimate within a few hours the repair yield of a memory product. An example of application to an actual SRAM product is discussed to illustrate in detail how our method can be used for critical area calculation and repair-yield modeling.
A new defect detection algorithm that compares grayscale images of actual patterns and an automatic visual inspection system implementing this algorithm have been developed. The objective is to detect defects reliably down to 0.3 μm in LSI photoresist patterns on a silicon wafer. To detect defects reliably while remaining uninfluenced by tiny differences between two images of satisfactory patterns, the images are matched in local windows by perturbing one image in the x‐y plane and in the brightness direction against the other image. The resulting unmatched regions are recognized as defects. One unique feature of the algorithm is its utilization of polarity changes in the subtracted images during the perturbation for deleting tiny differences between two images. All processing can be done in real time by local, one‐pass operators. The developed automatic visual inspection system has achieved a 100 percent detection rate for defects down to 0.3 μm.
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