TY - JOUR
T1 - Optical inspection of nanoscale structures using a novel machine learning based synthetic image generation algorithm
AU - Purandare, Sanyogita
AU - Zhu, Jinlong
AU - Zhou, Renjie
AU - Popescu, Gabriel
AU - Schwing, Alexander
AU - Goddard, Lynford L.
N1 - Publisher Copyright:
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067982406&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067982406&partnerID=8YFLogxK
U2 - 10.1364/OE.27.017743
DO - 10.1364/OE.27.017743
M3 - Article
C2 - 31252730
AN - SCOPUS:85067982406
SN - 1094-4087
VL - 27
SP - 17743
EP - 17762
JO - Optics Express
JF - Optics Express
IS - 13
ER -