Hardware Integrity Verification Through Forensic Reasoning Using Manufacturing
Hardware Integrity Verification Through Forensic Reasoning Using Manufacturing Images Dr. Eyal Weiss , Founder & CTO, Cybord Abstract Modern
Secure Hardware Assurance Using Visual AI on AOI Imaging of Electronic Assemblies
Author: Dr. Eyal Weiss, Founder & CTO, Cybord TL;DR ● A deep-learning framework (by Dr. Eyal Weiss; published under Springer Nature) for secure hardware
Electronic Components Provenance Assurance Through AI and Big Data in Assembly Processes – Eyal Weiss
TL;DR A method (by Cybord founder/CTO Dr. Eyal Weiss) for real-time authentication of components during assembly using AI and big data on pick-and-place bottom-side images. Core
AI-Powered Real-Time Inspection for Electronic Component Assembly
TL;DR A paper presenting real-time, inline inspection of electronic components using AI deep learning, aligned with IPC-A-610 and IPC-J-STD-001. It reuses existing pick-and-place
Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques | Eyal Weiss
Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques Abstract: This study presents an advanced methodology for improving electronic assembly quality through
Impact of Component Quality on Electronic Product Reliability and Environmental Sustainability | Preprint
Abstract: The electronics industry is a significant contributor to environmental challenges, generating approximately 50 million tons of electronic waste annually and high levels of carbon
Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning
Eyal Weiss * , Shir Caplan, Kobi Horn and Moshe Sharabi Technology Department, Cybord.ai, Tel-Aviv 6744332, Israel * Correspondence: eyal.w@cybord.ai Abstract: This paper introduces a
