Pixel-oriented database visualizations
ACM SIGMOD Record
Interactive visualization of serial periodic data
Proceedings of the 11th annual ACM symposium on User interface software and technology
Industrial evaluation of DRAM tests
DATE '99 Proceedings of the conference on Design, automation and test in Europe
Mining IC test data to optimize VLSI testing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualization of test information to assist fault localization
Proceedings of the 24th International Conference on Software Engineering
Large Datasets at a Glance: Combining Textures and Colors in Scientific Visualization
IEEE Transactions on Visualization and Computer Graphics
Harnessing Natural Textures for Multivariate Visualization
IEEE Computer Graphics and Applications
Engineering Perceptually Effective Visualizations for Abstract Data
Scientific Visualization, Overviews, Methodologies, and Techniques
Test Pattern Development and Evaluation for DRAMs with Fault Simulator RAMSIM
Proceedings of the IEEE International Test Conference on Test: Faster, Better, Sooner
Automatic Failure-Analysis System for High-Density DRAM
Proceedings of the IEEE International Test Conference on TEST: The Next 25 Years
Query by Attention: Visually Searchable Information Maps
IV '01 Proceedings of the Fifth International Conference on Information Visualisation
Information Visualization: Perception for Design
Information Visualization: Perception for Design
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This paper presents a technique that allows test engineers to visually analyze and explore within memory chip test data. We represent the test results from a generation of chips along a traditional 2D grid and a spiral. We also show correspondences in the test results across multiple generations of memory chips. We use simple geometric "glyphs" that vary their spatial placement, color, and texture properties to represent the critical attribute values of a test. When shown together, the glyphs form visual patterns that support exploration, facilitate discovery of data characteristics, relationships, and highlight trends and exceptions in the test data that are often difficult to identify with existing statistical tools.