Industrial approaches for performance evaluation using on-chip monitors

Zandrahimi, Mahroo; Debaud, Philippe; Castillejo, Armand; Al-Ars, Zaid

doi:10.1109/idt.2016.7843042

Cited by 1 publication

(1 citation statement)

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“…Since there are discrepancies in the responses of same PMBs from different test chips, the estimated correlation between the frequency of PMBs and the actual performance of the circuit could be very pessimistic, which results in wasting power and performance. Hence in terms of accuracy and tuning effort, online approaches always win [20].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Impact of Technology Scaling on Delay Testing for Low-Cost AVS

et al. 2019

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With the continued down-scaling of IC technology and increase in manufacturing process variations, it is becoming ever more difficult to accurately estimate circuit performance of manufactured devices. This poses significant challenges on the effective application of adaptive voltage scaling (AVS) which is widely used as the most important power optimization method in modern devices. Process variations specifically limit the capabilities of Process Monitoring Boxes (PMBs), which represent the current industrial state-of-the-art AVS approach. To overcome this limitation, in this paper we propose an alternative solution using delay testing, which is able to eliminate the need for PMBs, while improving the accuracy of voltage estimation. The paper shows, using simulation of ISCAS'99 benchmarks with 28nm FD-SOI library, that using delay test patterns result in an error of 5.33% for transition fault testing (TF), error of 3.96% for small delay defect testing (SDD), and an error as low as 1.85% using path delay testing (PDLY). In addition, the paper also shows the impact of technology scaling on the accuracy of delay testing for performance estimation during production. The results show that the 65nm technology node exhibits the same trends identified for the 28nm technology node, namely that PDLY is the most accurate, while, TF is the least accurate performance estimator.

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Section: Introductionmentioning

confidence: 99%