AI Article Synopsis
- Optical scatterometry is increasingly recognized in the semiconductor industry for deducing the geometry of optical diffraction structures from scattered light intensity.
- This study introduces an artificial neural network classifier designed to identify the geometrical structure of diffraction gratings based on their ellipsometric signatures before further analysis.
- The research focuses on two geometric shapes—sinusoidal and symmetric trapezoidal—using experimental samples from different substrates, highlighting the practical application of the neural network in real manufacturing scenarios.
Article Abstract
Optical scatterometry has been given much credit during the past few years in the semiconductor industry. The geometry of an optical diffracted structure is deduced from the scattered intensity by solving an inverse problem. This step always requires a previously defined geometrical model. We develop an artificial neural network classifier whose purpose is to identify the structural geometry of a diffraction grating from its measured ellipsometric signature. This will take place before the characterization stage. Two types of geometry will be treated: sinusoidal and symmetric trapezoidal. Experimental results are performed on two manufactured samples: a sinusoidal photoresist grating deposited on a glass substrate and a trapezoidal grating etched on a SiO2 substrate with periods of 2 microm and 0.565 microm, respectively.
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| http://dx.doi.org/10.1364/josaa.25.001661 | DOI Listing |
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