Supervised Learning Methods for Inspecting Semiconductors, Wafers, and Materials

Jul 18, 2023
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Deploying supervised learning methods for automated optical inspection (AOI) of semiconductor wafers and other materials enables process engineers to detect defects on bare and patterned samples faster and with increased accuracy than could be accomplished by human agents alone.

AOI systems can now classify anomalies using AI, effectively assigning causality to features of interest and providing accelerated feedback to improve fabrication processes. High quality inference from sparse data can be derived, eliminating the need for large training sets required by conventional methods. Through AI-powered processes, control data collected throughout inspection processes can be fed into a continuous cycle of feedback which is then formalized into an adaptable, self-regulating model. This model can be incorporated into structured processes with time dependency, reactions, extrusions, and material growth. It monitors production to predict and alert operators to errors in the process, adding an extra layer of security and ultimately assuming autonomous factory control.

Deep learning can bridge gaps between dispersed inspection nodes, enabling manufacturers to simultaneously make corrections and adjust for future errors in real time. These novel capabilities result in increased yields, high flexibility, reduced waste, and greater overall efficiency of the manufacturing process.

*** This presentation premiered during the 2023 Vision Spectra Conference. For more information on Photonics Media conferences, visit

About the presenter

Julie OrlandoJulie Orlando began working for Nanotronics’ co-founders Matthew and John Putman at their previous company, Tech Pro, Inc., where she played a key role in the hardware development, sales, and customer service departments. Tech Pro was acquired by Roper Industries in 2008. Orlando went on to run a division of Duramax, leading projects for major industrial entities, defense contractors, and branches of the military. She joined Nanotronics as employee number three, where she is currently the chief product officer. She has published several papers in both domestic and international journals and presented at various materials science conferences and symposia.
Materialsmachine visionVision SpectrasemiconductorsWafersautomationoptical inspection
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