Imperial College London

Dr Abd Al Rahman M. Abu Ebayyeh

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Teaching Fellow in Applied Machine Learning







308Electrical EngineeringSouth Kensington Campus





Publication Type

5 results found

Abu Ebayyeh AARM, Mousavi A, Danishvar S, Blaser S, Gresch T, Landry O, Müller Aet al., 2022, Waveguide quality inspection in quantum cascade lasers: A capsule neural network approach, Expert Systems with Applications, Vol: 210, Pages: 118421-118421, ISSN: 0957-4174

Journal article

Moustris GP, Kouzas G, Fourakis S, Fiotakis G, Chondronasios A, Abu Ebayyeh AARM, Mousavi A, Apostolou K, Milenkovic J, Chatzichristodoulou Z, Beckert E, Butet J, Blaser S, Landry O, Müller Aet al., 2022, Defect detection on optoelectronical devices to assist decision making: A real industry 4.0 case study, Frontiers in Manufacturing Technology, Vol: 2

<jats:p>This paper presents an innovative approach, based on industry 4.0 concepts, for monitoring the life cycle of optoelectronical devices, by adopting image processing and deep learning techniques regarding defect detection. The proposed system comprises defect detection and categorization during the front-end part of the optoelectronic device production process, providing a two-stage approach; the first is the actual defect identification on individual components at the wafer level, while the second is the pre-classification of these components based on the recognized defects. The system provides two image-based defect detection pipelines. One using low resolution grating images of the wafer, and the other using high resolution surface scan images acquired with a microscope. To automate the entire process, a communication middleware called Higher Level Communication Middleware (HLCM) is used for orchestrating the information between the processing steps. At the last step of the process, a Decision Support System (DSS) collects all information, processes it and labels it with additional defect type categories, in order to provide recommendations to the optoelectronical engineer. The proposed solution has been implemented on a real industrial use-case in laser manufacturing. Analysis shows that chips validated through the proposed process have a probability to lase at a specific frequency six times higher than the fully rejected ones.</jats:p>

Journal article

Abu Ebayyeh AARM, Danishvar S, Mousavi A, 2022, An Improved Capsule Network (WaferCaps) for Wafer Bin Map Classification Based on DCGAN Data Upsampling, IEEE Transactions on Semiconductor Manufacturing, Vol: 35, Pages: 50-59, ISSN: 0894-6507

Journal article

Ebayyeh AARMA, Mousavi A, 2020, A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry, IEEE Access, Vol: 8, Pages: 183192-183271

Journal article

Ahmad S, Salman SA, Abu Ebayyeh AARM, 2019, Design and Implementation of Education and Training Graphical User Interface (GUI) Based on NI LabVIEW for the FESTO MPS PA Compact Workstation, International Review of Automatic Control (IREACO), Vol: 12, Pages: 67-67, ISSN: 1974-6059

Journal article

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