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From wafer sort to yield learning is a long, complex process. Even worst because when scan chains fail, wafer sort adds negligible value. To address this gap a fast, reliable and accurate localization technology for scan chain fails that enable rapid translation of these into yield learning is presented.

Yield on specific designs often falls far short of predicted yield, especially at new technology nodes. Product-specific yield ramp is particularly challenging because the defects are, by definition, specific to the design, and often require some degree of design knowledge to isolate the failure. Despite the wide variety of advanced electrical failure analysis (EFA) techniques available today, they are not routinely applied during yield ramp. EFA techniques typically require a significant amount of test pattern customization, fixturing modification, or design knowledge. Unless the problem is critical, there is usually not time to apply advanced EFA techniques during yield ramp, despite the potential of EFA to provide valuable defect insight. We present a volume-oriented workflow integrating a limited set of electrical failure analysis (EFA) techniques. We believe this workflow will provide significant benefit by improving defect localization and identification beyond what is available using test-based techniques.

This paper presents the success story of the learning process by reporting four cases using four different failure analysis techniques. The cases covered are IDDQ leakage, power short, scan chain hard failure, and register soft failure. Hardware involved in the cases discussed are Meridian WS-DP, a wafer-level electrical failure analysis (EFA) system from DCG Systems, V9300 tester from Advantest, and a custom cable interface integrating WSDP and V9300 with the adaption of direct-probe platform that is widely deployed for SoC CP test. Four debug cases are reported in which various EFA techniques are proven powerful and effective, including photon emission, OBIRCH, Thermal Frequency Imaging, LVI, LVP, and dynamic laser stimulation.