Abstract: Semiconductor yield is modeled at the die level to predict die that are susceptible to early lifetime failure (ELF). A first die yield calculation is made from parametric data obtained from wafer testing in a semiconductor manufacturing process. A second die yield calculation is made from die location only. The difference between the first die yield calculation and the second die yield calculation is a prediction delta. Based on an evaluation of the first die yield calculation and the prediction delta, the likelihood of early lifetime failure can be identified and an acceptable level of die loss can be established to remove die from further processing.

Abstract: Semiconductor yield is modeled at the die level to predict die that are susceptible to early lifetime failure (ELF). A first die yield calculation is made from parametric data obtained from wafer testing in a semiconductor manufacturing process. A second die yield calculation is made from die location only. The difference between the first die yield calculation and the second die yield calculation is a prediction delta. Based on an evaluation of the first die yield calculation and the prediction delta, the likelihood of early lifetime failure can be identified and an acceptable level of die loss can be established to remove die from further processing.

Abstract: There is provided an improved testing system. More specifically, in one embodiment, there is provided a method including accessing machine overall equipment effectiveness or efficiency (OEE) data including machine generated operational event states of an automated testing (ATE) system and times the machine generated operational event states occurred, receiving operator OEE data including operator entered operational event states of the ATE and times the operator observed operational event states, and combining the machine OEE data and the operator OEE data to generate merge OEE data.