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A Classification Approach for an Accurate Analog/RF BIST Evaluation Based on the Process Parameters

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Abstract

Specifications of Radio Frequency (RF) analog integrated circuits have increased strictly as their applications tend to be more complicated and high test cost demanding. This makes them very expensive due to an increased test time and to the use of sophisticated test equipment. Alternative test measures, extracted by means of Built-In Self Test (BIST) techniques, are useful approaches to replace standard specification-based tests. One way to evaluate the efficiency of the CUT measures at the design stage is by estimating the Test Escapes (T E ) and the Yield Loss (Y L ) at ppm level. Unfortunately, an important number of Monte Carlo simulations must be run in order to guarantee their accuracy. For certain types of circuits, this requires many months or even years to generate millions of circuits. To overcome this limitation, we present in this paper a new technique where a small number of simulations is sufficient to reach an important precision. This method is based on a classification using machine learning methods, such as SVM and Neural Networks based classifiers to determine pass/fail regions. The proposed approach requires a few number of simulations only to determine the region separating the process parameters generating good and faulty, or pass and fail circuits. Then only this region is needed to estimate the test metrics without running any additional simulation. The proposed methodology is illustrated for the evaluation of a filter BIST technique.

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Authors and Affiliations

  1. Lab-STICC Laboratory, University of Brest, Brest, France

    Ahcène Bounceur & Reinhardt Euler

  2. LaMOS Research Unit, Department of Mathematics, University of Jijel, Jijel, Algeria

    Samia Djemai

  3. LaMOS Research Unit, Department of Operational Research, University of Bejaia, Bejaia, Algeria

    Belkacem Brahmi & Mohand Ouamer Bibi

Authors
  1. Ahcène Bounceur
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  2. Samia Djemai
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  3. Belkacem Brahmi
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  4. Mohand Ouamer Bibi
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  5. Reinhardt Euler
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Correspondence to Ahcène Bounceur.

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Responsible Editor: M. Barragan and K. Huang

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Bounceur, A., Djemai, S., Brahmi, B. et al. A Classification Approach for an Accurate Analog/RF BIST Evaluation Based on the Process Parameters. J Electron Test 34, 321–335 (2018). https://doi.org/10.1007/s10836-018-5730-0

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  • DOI: https://doi.org/10.1007/s10836-018-5730-0

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