Detektion av defekter i nanostrukturer med maskininlärning - En lösning med Autoencoders och Unsupervised Learning
Typ
Examensarbete för kandidatexamen
Program
Publicerad
2020
Författare
Carlsson, Eric
Cronquist, Olof
Eriksson, Oscar
Ulmestrand, Mattias
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This study aims to investigate the possibilities of using Machine Learning for detecting
fabrication defects in nanostructures. The structures investigated are microwave circuits
produced at the Department of microtechnology and nanoscience (MC2) at Chalmers
University of Technology. Firstly, a particularly defective circuit was investigated with
Logistic Regression, Dense Neural Networks, Convolutional Neural Networks as well
a Transfer Learning based method with ResNetV2 and Principal Component Analysis.
The distribution of defective and non-defective circuits was large and balanced enough
to achieve 100 % correct classification of the sections with almost all models. Two more
realistically defective circuits were further investigated, where the defective sections were
widely underrepresented. However, a Convolutional Autoencoder (CAE), trained with either
Supervised or Unsupervised Learning, was largely successful in separating defective
sections from non-defective ones with a clear boundary based on the reconstruction error
provided by the CAE. Furthermore, the CAE was in many cases able to locate the exact
positions of defects by marking the areas of maximum reconstruction error, and also flag
defective sections that previously was unsuccessful with manual inspection. The circuit
sections used for the CAE were automatically sampled from larger images of circuits. After
being sampled from the larger images of circuits, the sections were only downsampled
and not preprocessed in any other way. The success of the unsupervised approach is the
main achievement of the study, as all that is needed to train the model is completely uninvestigated
images. The approach is estimated to save several days of manually inspecting
whole circuits for defects.
Beskrivning
Ämne/nyckelord
Machine Learning, Unsupervised Learning, Convolutional Autoencoder, nanofabrication, microwave circuit