Abstract: A method optimizes photomask inspection. After masks are manufactured, the method predicts the likelihood that the masks will be defect free based on defect criteria, etch area, etch mode, and etch tool type associated with the masks. The method skips an initial mask inspection for masks that have a predictability value above a predetermined value and performs the initial mask inspection for masks that have a predictability value below the predetermined value. After initial inspection is preformed (or skipped), a pellicle is mounted on the mask and then all masks are inspected or reinspected for defects and foreign matter.

Abstract: A method optimizes photomask inspection. After masks are manufactured, the method predicts the likelihood that the masks will be defect free based on defect criteria, etch area, etch mode, and etch tool type associated with the masks. The method skips an initial mask inspection for masks that have a predictability value above a predetermined value and performs the initial mask inspection for masks that have a predictability value below the predetermined value. After initial inspection is preformed (or skipped), a pellicle is mounted on the mask and then all masks are inspected or reinspected for defects and foreign matter.

Abstract: A yield expectation determination is dynamically provided during manufacturing of a lot of integrated circuits. In one embodiment, the determination includes initially establishing a yield expectation for a lot, which can be based on a process grade, and adjusting the yield expectation during manufacturing based on test data from the kerf of a wafer. In addition, the yield expectation can be adjusted based on inspection data from optical and SEM inspection tools during manufacturing. Correlation coefficient models that correlate kerf data and inspection data to a yield expectation adjustment are used to dynamically adjust the yield expectation, resulting in a more accurate yield projection during manufacturing. The correlation coefficient models and/or process grade estimates are updated based on actual yield from a previous lot, thus further improving yield expectation accuracy.

Abstract: A statistical method is described for reliability selection of dies on semiconductor wafers using critical wafer yield parameters. This is combined with other data from the wafer or module level reliability screens (such as voltage screen or burn-in) to obtain the relative latent defect density. Finally the modeled results are compared with actual results to demonstrate confidence in the model.

Abstract: Method for burn-in testing of a wafer having a plurality of dies where the reliability of the fail rate is matched to meet a predetermined criteria. This is accomplished by selecting a subset of dies to be tested and tests are used to weed out the highest number of failures.

Abstract: A statistical method is described for reliability selection of dies on semiconductor wafers using critical wafer yield parameters. This is combined with other data from the wafer or module level reliability screens (such as voltage screen or burn-in) to obtain the relative latent defect density. Finally the modeled results are compared with actual results to demonstrate confidence in the model.