VLDB 2026 Research / reviewers in the wild / expert
Shi-Xuan Zheng
dblp:276/9012
· DBLP profile ↗
4ranked-venue papers
1as first author
2since 2021 · last 2022
0000-0002-1700-2198ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 4 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Electronic design automation · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Electronic design automation
hardware verification and test |
0.6 | 1 | 2022 | Efficient Test Compression Configuration Selection · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2022 |
Electronic design automation › hardware verification and test
test data compression |
0.6 | 1 | 2022 | Efficient Test Compression Configuration Selection · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2022 |
Methods — techniques the papers use, named apart from their topics
ATPG · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Accurate Estimation of Test Pattern Counts for a Wide-Range of EDT Input/Output Channel ConfigurationsabstractTest cost has become a critical issue for large industrial integrated circuits. Various test compression techniques have been adopted in the industry to reduce test cost. However, appropriate input and output channel counts must be selected to utilize the test compression technology best. This paper presents an efficient and effective method to estimate the test pattern counts under different compression configurations for the Embedded Deterministic Test (EDT) compression technique. In searching for the accurate estimation method, we build mathematical models that reveal the internal relationship among different compression configurations. The models are established based on novel theoretical analysis as well as actual experimental data. Accurate estimation of test pattern counts for a wide range of compression configurations can be obtained based on the results of only two ATPG runs. Experimental results on nine industrial circuits show that the average error rate of pattern count estimation is about 5%, with very few outliers. With the proposed method, a test compression designer can easily pick the best input and output channel configuration to fit the design needs. Shi-Xuan Zheng, Chung-Yu Yeh, Kuen-Jong Lee, Chen Wang 0014, Wu-Tung Cheng, Mark Kassab, Janusz Rajski, Sudhakar M. Reddy |
VTS | 1 |
| 2022 | Efficient Test Compression Configuration SelectionabstractTest costs for large industrial designs increase rapidly in recent years. On-chip test compression hardware has become a pragmatic technology to cut down the overall test costs by reducing the test data volume. Determining the input and output channel counts of test compression hardware that results in minimum test data volume is thus a critical issue. In this article, efficient methods to estimate test pattern counts for an extensive range of input/output counts are developed. These methods require only a small number of ATPG runs. The estimation results can then be utilized to determine the test data volume for each input/output configuration. The configuration with the estimated lowest test data volume thus can be determined. The pattern count results of each configuration for a design can also be used to determine the best suitable configuration when the design is to be embedded in an SoC system. Chong-Siao Ye, Shi-Xuan Zheng, Fong-Jyun Tsai, Chen Wang 0014, Kuen-Jong Lee, Wu-Tung Cheng, Sudhakar M. Reddy, Justyna Zawada, Mark Kassab, Janusz Rajski |
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. | 2 |
| 2020 | Estimation of Test Data Volume for Scan Architectures with Different Numbers of Input ChannelsabstractOver the past two decades, test data compression has become a de facto technology used in large industrial designs to reduce the overall test cost. During DFT planning, it is very important to understand the impact of using different numbers of input/output channels on test coverage, test cycles, and test data volume. In this paper, an efficient method to estimate the test data volume with different input channel counts using the Embedded Deterministic Test (EDT) compression technology is proposed. The results can then be used to quickly determine the scan configuration that results in the least or near least test data volume. With this method, the total ATPG run time can be reduced by a factor of more than 10X compared to the currently used trial-and-error method. Fong-Jyun Tsai, Chong-Siao Ye, Yu Huang 0005, Kuen-Jong Lee, Wu-Tung Cheng, Sudhakar M. Reddy, Mark Kassab, Janusz Rajski, Shi-Xuan Zheng |
ITC-Asia | 9 |
| 2020 | Prediction of Test Pattern Count and Test Data Volume for Scan Architectures under Different Input Channel ConfigurationsabstractAs the complexity of industrial integrated circuits continue to increase rapidly, test data compression has now become a de facto technology for large designs to reduce the overall test cost. During the design for test (DFT) planning, it is critical to understand the impact of using different numbers of input/output test channels on test coverage, test cycles, and test data volume. In this paper, two approaches to predict the test pattern counts and test data volumes with different input channel counts are presented, one with the compression tool able to generate channel-scaling patterns and the other without this capability. The results can be used to determine the scan test configuration that results in the smallest or near smallest test data volume. Experiments on industrial circuits show that the average error rates of pattern count prediction for most circuits are less than 10% for both approaches. The error rates of the predicted smallest data volumes are all less than 3.5%. The total ATPG run time can be reduced by a factor of more than 10X compared to the currently used trial-and-error approach. Fong-Jyun Tsai, Chong-Siao Ye, Kuen-Jong Lee, Shi-Xuan Zheng, Yu Huang 0005, Wu-Tung Cheng, Sudhakar M. Reddy, Mark Kassab, Janusz Rajski, Chen Wang 0014, Justyna Zawada |
ITC | 4 |