Sub 0.1 μm optical track width measurement

Richard J. Smith, Chung W. See, Michael Geoffrey Somekh, Andrew Yacoot

Research output: Journal article publicationConference articleAcademic researchpeer-review


In this paper, we will describe a technique that combines a common path scanning optical interferometer with artificial neural networks (ANN), to perform track width measurements that are significantly beyond the capability of conventional optical systems. Artificial neural networks have been used for many different applications. In the present case, ANNs are trained using profiles of known samples obtained from the scanning interferometer. They are then applied to tracks that have not previously been exposed to the networks. This paper will discuss the impacts of various ANN configurations, and the processing of the input signal on the training of the network. The profiles of the samples, which are used as the inputs to the ANNs, are obtained with a common path scanning optical interferometer. It provides extremely repeatable measurements, with very high signal to noise ratio, both are essential for the working of the ANNs. The characteristics of the system will be described. A number of samples with line widths ranging from 60nm-3μm have been measured to test the system. The system can measure line widths down to 60nm with a standard deviation of 3nm using optical wavelength of 633nm and a system numerical aperture of 0.3. These results will be presented in detail along with a discussion of the potential of this technique.
Original languageEnglish
Article number58580M
Pages (from-to)1-8
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 19 Dec 2005
Externally publishedYes
EventNano- and Micro-Metrology - Munich, Germany
Duration: 16 Jun 200517 Jun 2005


  • Artificial Neural Network
  • Interferometer
  • Line-width measurement

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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