Abstract

Microscopes and various forms of interferometers have been used for decades in optical metrology of objects that are typically larger than the wavelength of light λ. Metrology of sub-wavelength objects, however, was deemed impossible due to the diffraction limit. We report the measurement of the physical size of sub-wavelength objects with deeply sub-wavelength accuracy by analyzing the diffraction pattern of coherent light scattered by the objects with deep learning enabled analysis. With a 633 nm laser, we show that the width of sub-wavelength slits in an opaque screen can be measured with an accuracy of ∼λ/130 for a single-shot measurement or ∼λ/260 (i.e., 2.4 nm) when combining measurements of diffraction patterns at different distances from the object, thus challenging the accuracy of scanning electron microscopy and ion beam lithography. In numerical experiments, we show that the technique could reach an accuracy beyond λ/1000. It is suitable for high-rate non-contact measurements of nanometric sizes of randomly positioned objects in smart manufacturing applications with integrated metrology and processing tools.

Highlights

  • We report the measurement of the physical size of sub-wavelength objects with deeply sub-wavelength accuracy by analyzing the diffraction pattern of coherent light scattered by the objects with deep learning enabled analysis

  • With a 633 nm laser, we show that the width of sub-wavelength slits in an opaque screen can be measured with an accuracy of ∼λ/130 for a single-shot measurement or ∼λ/260 (i.e., 2.4 nm) when combining measurements of diffraction patterns at different distances from the object, challenging the accuracy of scanning electron microscopy and ion beam lithography

  • We report that the accuracy of single-shot measurements of linear dimensions of randomly positioned sub-wavelength objects scitation.org/journal/app of ∼λ/130 can be achieved by deep learning analysis of light scattered by the slits with a neural network trained on

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Summary

Introduction

We report that the accuracy of single-shot measurements of linear dimensions of randomly positioned sub-wavelength objects scitation.org/journal/app of ∼λ/130 can be achieved by deep learning analysis of light scattered by the slits with a neural network trained on

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