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Book Sections Year : 2023

Cell-Aware Model Generation Using Machine Learning

Abstract

Characterizing cell-internal defects of standard cell libraries is an essential step to ensure high test and diagnosis quality. However, such a characterization process, called cell-aware model generation, usually resorts to extensive electrical defect simulations that are costly in terms of run time and utilization of SPICE simulator licenses. Typically, the generation time of cell-aware models for few hundreds of cells may reach up to several months considering a single SPICE license. This chapter presents a methodology that does not use any electrical defect simulation to predict the response of a cell-internal defect once it is injected in a standard cell. More widely, this methodology uses existing cell-aware models (generated from electrical simulations) from various standard cell libraries and technologies to predict cellaware models (learning-based) for new standard cells independently of the technology. Experiments done on several industrial cell libraries using different technologies demonstrate the accuracy and performance of the prediction method.
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Dates and versions

lirmm-03986553 , version 1 (13-02-2023)

Identifiers

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Pierre D’hondt, Aymen Ladhar, Patrick Girard, Arnaud Virazel. Cell-Aware Model Generation Using Machine Learning. Frontiers of Quality Electronic Design (QED), Springer International Publishing, pp.227-257, 2023, 978-3-031-16344-9. ⟨10.1007/978-3-031-16344-9_6⟩. ⟨lirmm-03986553⟩
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