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Automatic Defect Sizing Gives Near-SEM Accuracy by Alexander E. Braun, Associate Editor, Semiconductor International
In-line defect inspection and classification’s goals are defect reduction and yield prediction. Some fabs consider all defects a problem and aim to reduce overall defectivity levels. Others focus yield prediction to enable key decisions, such as increasing wafer starts, to compensate for an expected yield loss and scrapping lots early on in the process to prevent a costly investment in wafers with low expected yield. Still others do both.
The industry has developed complex yield models to make accurate yield predictions. These vary from fab to fab, with many customized to suit a particular IC manufacturer’s product mix and device types. Regardless of the model, however, three inputs are needed to accurately predict yield in all models: the number of defects detected in-line, their classifications and their size.
sophisticated, if the sizing is inaccurate or fudged as a default value, the model produces a “blurry” yield estimate. Traditionally, the most accurate method to get accurate sizing data has been SEM measurement. This requires moving the wafer out of the inspector to an off-line
Today’s patterned wafer inspection tools and ADC systems have made the first two inputs relatively accurate. However, sizing is generally inaccurate and can result in erroneous yield predictions (figure 1) because fabs relied on in-line sizing outputed by inspection tools. In-line size error can vary considerably, depending on the inspector used, with darkfield tools being less accurate than brightfield. And even brightfield systems’ sizing capability is inherently limited by the inspection pixels used, which are typically large (0.62 to 0.39 µm) because of manufacturing throughput constraints associated with smaller, high-resolution pixels. An analogy to the situation is the “image stabilization” feature in video cameras. Before it, regardless of how good the optics, zoom, electronics, tape or other camera components were, if the operator moved while shooting, the image was blurred. Yield models are the same way: no matter how 34
Autumn 1999
Yield Management Solutions
F i g u re 1. By providing near-SEM-equivalent accuracy, the HRDC sizing module enables users to detect small sizing errors that would otherw i s e result in large yield prediction errors, leading to better processing decisions.