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- W4387123973 abstract "This work presents the proposal and feasibility evaluation of a machine learning (ML) based method for test generation of analog and mixed-signal circuits. The method first relies on the use of a previously proposed low-cost automatic test generation tool (based on spice simulation) to simulate the circuit specifications and low cost parametric measures (in this case, DC values of circuit nodes under different input stimuli). Variability data, based on Monte Carlo Simulation is also generated by the tool, by using an open source spice simulator and the appropriate foundry models of the transistors. With this dataset in hand, the method consists on training ML models to relate the low cost DC node measurements with the circuit specifications, configuring a well-known indirect test strategy. The models are evaluated and those with the best performance are selected, resulting in a set of low-cost measurements and ML algorithms that represent an indirect test strategy to predict the CUT tested specifications. The considered case study is a fully-differential integrated filter designed on a commercial 180 nm process. Results show a R <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> score equal to 0.76, 0.76 and 0.98 for the DC gain, cutoff frequency and slew rate parameters, respectively. A total of 19 indirect measurements were selected for performing the tests. Finally, the presented method facilitates the implementation of the indirect test in analog circuits. A wide range of design parameters can be explored, selecting those that best contribute to the quality of the indirect test." @default.
- W4387123973 created "2023-09-29" @default.
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- W4387123973 date "2023-08-28" @default.
- W4387123973 modified "2023-10-18" @default.
- W4387123973 title "Towards a Machine Learning Based Method for Indirect Test Generation of Mixed-Signal Circuits" @default.
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- W4387123973 doi "https://doi.org/10.1109/sbcci60457.2023.10261952" @default.
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