STATISTICAL METHODS USAGE TO IMPROVE DIE WAFERS RADIATION MONITORING EFFICIENCY IN MASS PRODUCTION PROCESS
Die wafers radiation monitoring is a mandatory (for IC’s with radiation hardness requirements) part of product quality control in mass production and a way to confirm that the technological process is able to meet the specified hardness requirements throughout the product life cycle, starting with R...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Joint Stock Company "Experimental Scientific and Production Association SPELS
2025-07-01
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| Series: | Безопасность информационных технологий |
| Subjects: | |
| Online Access: | https://bit.spels.ru/index.php/bit/article/view/1831 |
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| Summary: | Die wafers radiation monitoring is a mandatory (for IC’s with radiation hardness requirements) part of product quality control in mass production and a way to confirm that the technological process is able to meet the specified hardness requirements throughout the product life cycle, starting with R&D, die wafers fabrication and ICs mass production. In the current practice, die wafers radiation testing as part of the quality control system for serial production of microcircuits is one of the most resource-intensive and time-consuming product tests. It practically repeats radiation testing methods and algorithms during product development, without taking into account initially received test data from the previous product's life cycle stage, as well as previously obtained test results of all earlier die wafer tests during its mass production. In addition, there is no die wafers radiation hardness monitoring standard procedure, which leads to the variability in test methods and results provided by various test centers. The paper demonstrates that statistical patterns should be used during testing, which, on one hand, simplify the die wafers inspection algorithm in mass production, and, on the other hand, identify undeclared corrections of IC’s schematic and layout design as well as the technological process variations. |
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| ISSN: | 2074-7128 2074-7136 |