Francesco Minichelli
Machine Learning for Early Defect Detection in Automotive Semiconductors.
Rel. Riccardo Cantoro, Nicolo' Bellarmino. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2024
Abstract
In the automotive sector, semiconductors utilized in modern vehicles undergo rigorous testing prior to being supplied to end-users. This testing phase represent an important phase to guarantee that the semiconductors meet the high standards required in this domain, as well as to avert potential safety concerns that could arise from faulty semiconductors. This phase must be well design and without possible escapes. In fact, the industry defines specific standards on the numbers of defects per million. However, the testing phase accounts for a substantial portion of the total cost of a microcontroller and can create production bottlenecks. The testing phase is generally divided into two stages: pre-packaging (front end) and post-packaging (back end), with the latter being more time-consuming and expensive.
To address these challenges, the goal of this master's thesis project is to identify non-compliant semiconductors at an early stage, prior to packaging, in the front end
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