Display options
Cite
Smith RJ, See CW, Somekh MG, et al. Use of artificial neural networks on optical track width measurements. Appl Opt. 2007;46(22):4857-66doi: 10.1364/ao.46.004857.
Smith, R. J., See, C. W., Somekh, M. G., & Yacoot, A. (2007). Use of artificial neural networks on optical track width measurements. Applied optics, 46(22), 4857-66. https://doi.org/10.1364/ao.46.004857
Smith, Richard J, et al. "Use of artificial neural networks on optical track width measurements." Applied optics vol. 46,22 (2007): 4857-66. doi: https://doi.org/10.1364/ao.46.004857
Smith RJ, See CW, Somekh MG, Yacoot A. Use of artificial neural networks on optical track width measurements. Appl Opt. 2007 Aug 01;46(22):4857-66. doi: 10.1364/ao.46.004857. PMID: 17676087.
Copy Download .nbib Format: NLM
Share it on

Appl Opt. 2007 Aug 01;46(22):4857-66. doi: 10.1364/ao.46.004857.

Use of artificial neural networks on optical track width measurements.

Applied optics

Richard J Smith, Chung W See, Mike G Somekh, Andrew Yacoot

Affiliations

  1. School of Electrical and Electronic Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD,United Kingdom. [email protected]

PMID: 17676087 DOI: 10.1364/ao.46.004857

Abstract

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

Publication Types