Machine Learning Support for Diagnosis of Analog Circuits

Haralampos-G. Stratigopoulos (1)

1 CIAN - Circuits Intégrés Numériques et Analogiques

We discuss the state-of-the-art on fault diagnosis for analog circuits with a focus on techniques that leverage machine learning. For a chip that has failed either in post-manufacturing testing or in the field of operation, fault diagnosis is launched to identify the root-cause of failure at sub-block level and transistor-level. In this context, machine learning can be used to build a smart system that predicts the fault that has occurred from diagnostic measurements extracted on the chip. We discuss the different elements of a diagnosis flow for analog circuits, including fault modeling, fault simulation, diagnostic measurement extraction and selection, and the machine learning algorithms that compose the prediction system. We also demonstrate a machine learning-based diagnosis flow on an industrial case study.

Domaines: Micro et nanotechnologies/Microélectronique; Intelligence artificielle [cs.AI]; Ingénierie assistée par ordinateur

Haralampos-G. Stratigopoulos. Machine Learning Support for Diagnosis of Analog Circuits. Machine Learning Support for Fault Diagnosis of System-on-Chip, Springer Nature, 2023. ⟨hal-03872972⟩