The nature of statistical learning theory
The nature of statistical learning theory
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A general framework for spatial correlation modeling in VLSI design
Proceedings of the 44th annual Design Automation Conference
RF specification test compaction using learning machines
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Toward efficient spatial variation decomposition via sparse regression
Proceedings of the International Conference on Computer-Aided Design
Proceedings of the International Conference on Computer-Aided Design
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
Spatial correlation modeling for probe test cost reduction in RF devices
Proceedings of the International Conference on Computer-Aided Design
Spatial estimation of wafer measurement parameters using Gaussian process models
ITC '12 Proceedings of the 2012 IEEE International Test Conference (ITC)
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In an effort to reduce the cost of specification testing in analog/RF circuits, spatial correlation modeling of wafer-level measurements has recently attracted increased attention. Existing approaches for capturing and leveraging such correlation, however, rely on the assumption that spatial variation is smooth and continuous. This, in turn, limits the effectiveness of these methods on actual production data, which often exhibits localized spatial discontinuous effects. In this work, we propose a novel approach which enables spatial correlation modeling of wafer-level analog/RF tests to handle such effects and, thereby, to drastically reduce prediction error for measurements exhibiting discontinuous spatial patterns. The core of the proposed approach is a k-means algorithm which partitions a wafer into k clusters, as caused by discontinuous effects. Individual correlation models are then constructed within each cluster, revoking the assumption that spatial patterns should be smooth and continuous across the entire wafer. Effectiveness of the proposed approach is evaluated on industrial probe test data from more than 3,400 wafers, revealing significant error reduction over existing approaches.