Abdul Qadeer Rehan
University of Richmond
NIST Research Intern
National Institute of Standards and Technology (NIST)
With decreasing feature dimensions, increasing layout complexity, and greater material variations, undetected semiconductor patterning defects can have both technological and financial consequences in nanoelectronics fabrication. Optical methods are ideal for the fast, non-destructive identification of defect locations, but separating defect-based signals from measurement noise is a daunting task. Data-driven Machine Learning has been successfully applied to classifying simulated images,1 and our project has optimized a dataset of experimental images collected at 193 nm wavelength for enhanced processing using supervised neural networks. Images of intentional defect arrays have been obtained using a scatterfield microscope and processed here to enhance the training of convolutional neural networks. Initial results are presented showing the successful binary classification of defect and no-defect examples with discussion of the imbalanced costs due to false identifications. 1) Mark-Alexander Henn, Hui Zhou, Richard M. Silver, Bryan M. Barnes, "Applications of machine learning at the limits of form-dependent scattering for defect metrology," Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 109590Z (26 March 2019); doi: 10.1117/12.2517285
Growing up in Karachi, Pakistan I left home at the young age of 16 to attend the United World College of the Atlantic in Wales, before starting my undergraduate studies at the University of Richmond, Virginia to major in Computer Science and Physics. Over the past three years in college, I have been working under the supervision of Dr. Mariama Rebello de Sousa Dias, researching the optical properties of Aluminum-Gold thin films. This early exposure to the field of scientific research has driven me to strive to find and solve the unknown.
Outside of research, I work as a Sustainability Dining Intern at the Office of Sustainability on campus. My work involves implementing sustainable dining initiatives and food recovery programs at dining locations across campus. I also serve as my college's SPS chapter treasurer and a peer tutor for Computer Science and Math courses. Beyond work and research, I like to spend time exploring music, playing cricket and experiencing new cuisines.