The nature of statistical learning theory
The nature of statistical learning theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Non-RF to RF Test Correlation Using Learning Machines: A Case Study
VTS '07 Proceedings of the 25th IEEE VLSI Test Symmposium
A general framework for spatial correlation modeling in VLSI design
Proceedings of the 44th annual Design Automation Conference
Confidence Estimation in Non-RF to RF Correlation-Based Specification Test Compaction
ETS '08 Proceedings of the 2008 13th European Test Symposium
Proceedings of the 2009 International Conference on Computer-Aided Design
Prediction of analog performance parameters using fast transient testing
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Error Moderation in Low-Cost Machine-Learning-Based Analog/RF Testing
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Handling discontinuous effects in modeling spatial correlation of wafer-level analog/RF tests
Proceedings of the Conference on Design, Automation and Test in Europe
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Test cost reduction for RF devices has been an ongoing topic of interest to the semiconductor manufacturing industry. Automated test equipment designed to collect parametric measurements, particularly at high frequencies, can be very costly. Together with lengthy set up and test times for certain measurements, these cause amortized test cost to comprise a high percentage of the total cost of manufacturing semiconductor devices. In this work, we investigate a spatial correlation modeling approach using Gaussian process models to enable extrapolation of performances via sparse sampling of probe test data. The proposed method performs an order of magnitude better than existing spatial sampling methods, while requiring an order of magnitude less time to construct the prediction models. The proposed methodology is validated on manufacturing data using 57 probe test measurements across more than 3,000 wafers. By explicitly applying probe tests to only 1% of the die on each wafer, we are able to predict probe test outcomes for the remaining die within 2% of their true values.