Leveraging Machine Learning in Semiconductor Yield Analysis


Searching through wafer maps looking for spatial patterns is not only a very time-consuming task to be done manually, it’s also prone to human oversight and error, and nearly impossible in a large fab where there are thousands of wafers a day being processed. We developed a tool that applies automatic spatial pattern detection algorithms using ML, parametrizing pattern recognition and clas... » read more

Using BDA To to Predict SAQP Pitch Walk


A new technical paper titled "Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning" was published by researchers at IBM TJ Watson Research Center and Rensselaer Polytechnic Institute. Find the technical paper here. Published November 2022.  Open Access. Scott D. Halle, Derren N. Dunn, Allen H. Gabor, Max O. Bloomfield, and Mark Sh... » read more

Next Steps For Improving Yield


Chipmakers are ramping new tools and methodologies to achieve sufficient yield faster, despite smaller device dimensions, a growing number of systematic defects, immense data volumes, and massive competitive pressure. Whether a 3nm process is ramping, or a 28nm process is being tuned, the focus is on reducing defectivity. The challenge is to rapidly identify indicators that can improve yield... » read more

Test Connections Clean Up With Real-Time Maintenance


Test facilities are beginning to implement real-time maintenance, rather than scheduled maintenance, to reduce manufacturing costs and boost product yield. Adaptive cleaning of probe needles and test sockets can extend equipment lifetimes and reduce yield excursions. The same is true for load board repair, which is moving toward predictive maintenance. But this change is much more complicate... » read more