JOURNAL ARTICLE
Prediction of the Test Yield of Future Integrated Circuits Through the Deductive Estimation Method.
Published In: Journal of Circuits, Systems & Computers, 2023, v. 32, n. 12. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Yeh, Chung-Huang; Chen, Jwu E. 3 of 3
Abstract
In the past 20 years, semiconductor manufacturing technology has advanced rapidly, but the advancement of integrated circuit (IC) testers has been slow. Using obsolete testers to inspect advanced wafers has become a significant challenge for test manufacturers. In this research, we used DITM (digital IC testing model) to discuss the impact of the test guardband (TGB) on quality and yield. Considering the interaction between semiconductor fabrication capability parameters and test capability parameters, we proposed an estimation method [deductive estimation method (DEM)] to analyze the electrical distribution changes of products after chip production and deduce the yield of future products. The deductive estimation method can correctly depict the future test yield (Y t) curve using the chip frequency data published by IRDS (International Roadmap for Devices and Systems) in 2017. Furthermore, test manufacturers can measure whether the current test capabilities can cope with future semiconductor chip manufacturing capabilities by predicting the trends. Next, test manufacturers can maintain high-quality and high-yield chip output by pre-adjusting the hardware testing capabilities of ATE (automated test equipment) or proposing more effective chip testing methods. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Circuits, Systems & Computers. 2023/08, Vol. 32, Issue 12, p1
- Document Type:Article
- Subject Area:Engineering
- Publication Date:2023
- ISSN:0218-1266
- DOI:10.1142/S021812662350202X
- Accession Number:164798903
- Copyright Statement:Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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