Finding Frameworks For End-To-End Analytics


End-to-end analytics can improve yield and ROI on tool purchases, but reaping those benefits will require common data formats, die traceability, an appropriate level of data granularity — and a determination of who owns what data. New standards, guidelines, and consortium efforts are being developed to remove these barriers to data sharing for analytics purposes. But the amount of work req... » read more

Removing Barriers For End-To-End Analytics


Parties are coming together, generating guidelines for sharing data from IC design and manufacturing through end of life, setting the stage for true end-to-end analytics. While the promise of big data analytics is well understood, data sharing through the semiconductor supply chain has been stymied by an inability to link together data sources throughout the lifecycle of a chip, package, or ... » read more

Where And When End-to-End Analytics Works


With data exploding across all manufacturing steps, the promise of leveraging it from fab to field is beginning to pay off. Engineers are beginning to connect device data across manufacturing and test steps, making it possible to more easily achieve yield and quality goals at lower cost. The key is knowing which process knob will increase yield, which failures can be detected earlier, and wh... » read more

Big Payback For Combining Different Types Of Fab Data


Collecting and combining diverse data types from different manufacturing processes can play a significant role in improving semiconductor yield, quality, and reliability, but making that happen requires integrating deep domain expertise from various different process steps and sifting through huge volumes of data scattered across a global supply chain. The semiconductor manufacturing IC data... » read more