The recent advancements in the fields of artificial intelligence (AI) and machine learning
(ML) have affected several research fields, leading to improvements that could not have …

Feature selection has been an important research area in data mining, which chooses a
subset of relevant features for use in the model building. This paper aims to provide an …

The insistent trend in today's nanoscale technology, to keep abreast of the Moore's law, has
been continually opening up newer challenges to circuit designers. With rapid downscaling …

Integrated circuit (IC) testing presents complex problems that, when ICs become large, are
exceptionally difficult to solve by traditional computing techniques. To deal with …

The pursuit of accurate diagnosis with good resolution is driven by yield learning during both
early bring-up and production excursions. Unfortunately, fault callouts from diagnosis tools …

High integration densities and design complexity of printed-circuit boards make board-level
functional fault identification extremely difficult. Machine learning provides an opportunity to …

Root-cause analysis for integrated systems has become increasingly challenging due to
their growing complexity. To tackle these challenges, machine learning (ML) has been …

Numerous machine learning (ML), and more recently, deep-learning (DL)-based
approaches, have been proposed to tackle scalability issues in electronic design …

High integration densities and design complexity make board-level functional fault diagnosis
extremely difficult. Machine-learning techniques can identify functional faults with high …

The ever-increasing complexity of integrated circuits inevitably leads to high test cost.
Adaptive testing provides an effective solution for test-cost reduction; this testing framework …