Optical inspection of nanoscale structures using a novel machine learning based synthetic image generation algorithm

Sanyogita Purandare, Jinlong Zhu, Renjie Zhou, Gabriel Popescu, Alexander Schwing, Lynford L. Goddard

Research output: Contribution to journalArticle


In this paper, we present a novel interpretable machine learning technique that uses unique physical insights about noisy optical images and a few training samples to classify nanoscale defects in noisy optical images of a semiconductor wafer. Using this technique, we not only detected both parallel bridge defects and previously undetectable perpendicular bridge defects in a 9-nm node wafer using visible light microscopy [Proc. SPIE 9424, 942416 (2015)], but we also accurately classified their shapes and estimated their sizes. Detection and classification of nanoscale defects in optical images is a challenging task. The quality of images is affected by diffraction and noise. Machine learning techniques can reduce noise and recognize patterns using a large training set. However, for detecting a rare “killer” defect, acquisition of a sufficient training set of high quality experimental images can be prohibitively expensive. In addition, there are technical challenges involved in using electromagnetic simulations and optimization of the machine learning algorithm. This paper proposes solutions to address each of the aforementioned challenges.

Original languageEnglish (US)
Pages (from-to)17743-17762
Number of pages20
JournalOptics Express
Issue number13
StatePublished - Jan 1 2019


ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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