On Approaching Multivariate IC Pre-silicon Verification Using ML-based Adaptive Algorithms

This paper introduces several solutions for multivariate extension of a previously designed single response adaptive pre-silicon integrated circuit verification approach employing machine learning algorithms. These techniques aim to achieve the most accurate identification of worst-case circuit beha...

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Bibliographic Details
Published in2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS) pp. 1 - 3
Main Authors Rusu, Alecsandra, David, Emilian, Topa, Marina-Dana, Grosu, Vasile, Buzo, Andi, Pelz, Georg
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.07.2024
Subjects
Accuracy
Adaptation models
Gaussian Process Regression
IC pre-silicon verification
Integrated circuit modeling
Machine learning
Machine learning algorithms
Regulators
System analysis and design
Voltage control
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Summary:This paper introduces several solutions for multivariate extension of a previously designed single response adaptive pre-silicon integrated circuit verification approach employing machine learning algorithms. These techniques aim to achieve the most accurate identification of worst-case circuit behavior through simultaneously modeling multiple electrical parameters (EP). The effectiveness of the proposed methods was validated through extensive testing on a large and diverse set of synthetic test functions that intend to replicate the behavior of real circuits. The algorithms consistency and accuracy are also validated on a real Low Dropout Voltage Regulator (LDO) circuit.
ISSN:1942-9401
DOI:10.1109/IOLTS60994.2024.10616057