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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
š Hi! I'm Riccardo
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Posts
SHARPER Night 2023
Published:
Segmentation and Pose estimation on YOLOv8 demonstration at SHARPER Night 2023
Essay competition at ICVSS 2023
Published:
Alan Turing making the Turing test on the desk
Hello World!
Published:
A robot thinks like in The Thinker pose of Auguste Rodin
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Deep Learning for Intelligent Wafer Defect Pattern Recognition System Permalink
Published in Ital-IA, 2022
Explainable Deep Learning System for Advanced Silicon and Silicon Carbide Electrical Wafer Defect Map Assessment Permalink
Published in IEEE Access, 2022
Abstract
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 āUnsupervised Learning Blockā embeds ad-hoc designed Dimensionality Reduction combined with Clustering and a Metrics-driven Classification Sub-Systems. The proposed āSupervised Learning Blockā 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%).
Recommended citation: R. E. Sarpietro et al. "Explainable Deep Learning System for Advanced Silicon and Silicon Carbide Electrical Wafer Defect Map Assessment," IEEE Access, vol. 10, pp. 99102-99128, 2022 doi: 10.1109/ACCESS.2022.3204278
Hybrid Deep Learning Pipeline for Advanced Electrical Wafer Defect Maps Assessment Permalink
Published in AEIT, 2022
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
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