Identification of Spatial Fault Patterns in Semiconductor Wafers

Metadata Updated: March 23, 2019


The semiconductor industry is constantly searching for new ways to increase the rate of both process development and yield learning. As more data is being collected and stored throughout the chip manufacturing process, it has become increasingly more difficult to analyze yield signals using traditional statistical methods. Most of the serious yield issues manifest themselves as non-random electrical failure maps. Our semi-supervised fault detection framework has elements of Spatial Signature Analysis (SSA) to capture yield signals for very large datasets without losing the critical details typically involved with summarization techniques. It includes signature detection, de-noising, clustering, and purification that allow one to create a true spatial response metric of the yield issue. Once this has been accomplished, one can load process data to join with the spatial response and invoke customized rule induction algorithms that generate a set of hypotheses - likely process causes for a specific spatial target response. The framework has been successfully used at Intel and represents an example of the growing influence of modern statistical learning in the semiconductor industry.


Dr. Eugene Tuv, Intel

Dr. Eugene Tuv is a Senior Staff Research Scientist in the Logic Technology Department at Intel. His research interests include supervised and unsupervised non-parametric machine learning with massive heterogeneous data. Prior to Intel he worked as a research scientist in the Institute of Nuclear Research, Ukrainian Academy of Science. He holds postgraduate degrees in Mathematics and Applied Statistics.

Access & Use Information

Public: This dataset is intended for public access and use. License: U.S. Government Work

Downloads & Resources


Metadata Created Date August 1, 2018
Metadata Updated Date March 23, 2019
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date March 23, 2019
Publisher Dashlink
Unique Identifier DASHLINK_59
Elizabeth Foughty
Maintainer Email
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Datagov Dedupe Retained 20190322235447
Harvest Object Id d78f8b65-e474-4327-8998-4e0babbc2569
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2010-09-10
Homepage URL
Data Last Modified 2018-07-19
Program Code 026:029
Source Datajson Identifier True
Source Hash 308510ca5d83d1593c0651bb00288239a99b66aa
Source Schema Version 1.1

Didn't find what you're looking for? Suggest a dataset here.