English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The recent increasing demand of Silicon-on-Chip devices has triggered a significant impact on the industrial processes of leading semiconductor companies. The semiconductor industry is redesigning internal technology processes trying to optimize costs and production yield. To achieve this target a key role is played by the intelligent early wafer defects identification task. The Electrical Wafer Sorting (EWS) stage allows an efficient wafer defects analysis by processing the visual map associated to the wafer. The goal of this contribution is to provide an effective solution to perform automatic evaluation of the EWS defect maps. The proposed solution leverages recent approaches of deep learning both supervised and unsupervised to perform a robust EWS defect patterns classification in different device technologies including Silicon and Silicon Carbide. This method embeds an end-to-end pipeline for supervised EWS defect patterns classification including a hierarchical unsupervised system to assess novel defects in the production line. The implemented &#x201C;Unsupervised Learning Block&#x201D; embeds ad-hoc designed Dimensionality Reduction combined with Clustering and a Metrics-driven Classification Sub-Systems. The proposed &#x201C;Supervised Learning Block&#x201D; includes a Convolutional Neural Network trained to perform a supervised classification of the Wafer Defect Maps (WDMs). The proposed system has been evaluated on several datasets, showing effective performance in the classification of the defect patterns (average accuracy about 97&#x0025;).

Explainable Deep Learning System for Advanced Silicon and Silicon Carbide Electrical Wafer Defect Map Assessment