NDLI: On-Chip Structures for $F_{max}$ Binning and Optimization

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On-Chip Structures for $F_{max}$ Binning and Optimization

Content Provider	MDPI
Author	Zhang, Dongrong Ren, Qiang Su, Donglin
Copyright Year	2022
Abstract	Process variations during manufacturing lead to differences in the performance of the chips. In order to better utilize the performance of the chips, it is necessary to perform maximum operation frequency ( $F_{m a x}$ ) tests to place the chips into different speed bins. For most $F_{m a x}$ tests, significant efforts are put in place to reduce test cost and improve binning accuracy; e.g., our conference paper published in ICICM 2017 presents a novel binning sensor for low-cost and accurate speed binning. However, by promoting chips placed at the lower bins, because of conservative binning, into higher bins, the overall profit can greatly increase. Therefore, this paper, extended based on a conference paper, presents a novel and adaptive methodology for speed binning, in which the paths impacting the speed bin of a specific IC are identified and adapted by our proposed on-chip Binning Checker and Binning Adaptor. As a result, some parts at a bin margin can be promoted to higher bins. The proposed methodology can be used to optimize the $F_{m a x}$ yield of a digital circuit when it has redundant timing in clock tree, and it can be integrated into current $F_{m a x}$ tests with low extra cost. The proposed adaptive system has been implemented and validated on five benchmarks from ITC, ISCAS89, and OpenSPARCT2 core on 28 nm Altera FPGAs. Measurement results show that the number of higher bin chips is improved by 7–16%, and our cost analysis shows that the profit increase is between 1.18% and 3.04%.
Starting Page	1382
e-ISSN	14248220
DOI	10.3390/s22041382
Journal	Sensors
Issue Number	4
Volume Number	22
Language	English
Publisher	MDPI
Publisher Date	2022-02-11
Access Restriction	Open
Subject Keyword	Sensors Hardware and Architecturee On-chip Sensor Speed Binning Yield Optimization Dynamic Adaptation
Content Type	Text
Resource Type	Article

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