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Maximizing Yield for Approximate Integrated Circuits

Abstract : Approximate Integrated Circuits (AxICs) have emerged in the last decade as an outcome of Approximate Computing (AxC) paradigm. AxC focuses on efficiency of computing systems by sacrificing some computation quality. As the AxICs spread, consequent challenges to test them arose. On the other hand, the opportunity to increase the production yield emerged in the AxICs context. Indeed, some particular defects in the manufactured AxIC might not catastrophically impact the final circuit quality. Therefore, some defective AxICs might still be acceptable. Efforts to detect favorable conditions to consider defective AxICs as acceptable-with the goal to increase the production yield-have been done in last years. Unfortunately, the final achieved yield gain is often not as high as expected. In this work, we propose a methodology to actually achieve a yield gain as close as possible to expectations, by proposing a technique to suitably apply tests to AxICs. Experiments carried out on state-of-the-art AxICs show yield gain results very close to the expected ones (i.e., between 98% and 100% of the expectations).
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03036002
Contributor : Isabelle Gouat <>
Submitted on : Wednesday, December 2, 2020 - 2:34:09 PM
Last modification on : Monday, December 7, 2020 - 7:28:05 PM
Long-term archiving on: : Wednesday, March 3, 2021 - 7:29:07 PM

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Marcello Traiola, Arnaud Virazel, Patrick Girard, Mario Barbareschi, Alberto Bosio. Maximizing Yield for Approximate Integrated Circuits. Design, Automation & Test in Europe Conference & Exhibition (DATE), Mar 2020, Grenoble, France. pp.810-815, ⟨10.23919/DATE48585.2020.9116341⟩. ⟨lirmm-03036002⟩

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