Deep learning and superoscillatory speckles empowered multimode fiber probe for in situ nano-displacement detection and micro-imaging

TL;DR

This paper introduces a new non-contact system using a fiber probe and AI to detect tiny movements and images in tight spaces with high accuracy.

Contribution

A novel multimode fiber probe combining superoscillatory speckles and deep learning for in situ nano-displacement detection and micro-imaging.

Findings

The system achieves 10 nm resolution and 99.95% accuracy in displacement measurements.

It enables robust sensing in confined spaces with varying fiber bending and metal materials.

The probe's imaging capability is experimentally validated for potential use in lithography and micro-endoscopy.

Abstract

High-precision metrology has laid the foundation for semiconductor fabrication and life sciences. However, existing displacement measurement approaches are incapable of performing flexible probing within complex equipment interiors. Here, we present a in situ, non-contact nano-displacement measurement approach. Leveraging a multimode fiber probe empowered by deep learning, fine feature information can be efficiently extracted from superoscillatory speckles, achieving single-ended detection with 10 nm resolution and 99.95% accuracy. A physical model is established to correlate the displacement with higher-order modes proportion in the fiber. Sub-millimeter-sized probe enables detecting targets with different structures in confined spaces. Robust recognition is achieved through joint learning, under varying fiber bending conditions and different metal materials. With extreme compression ratios of less than 0.1%, the system delivers high accuracy, low training costs, and high-speed processing. The imaging capability of the probe is also experimentally validated, proving potential as a powerful tool in applications such as lithography, weak force sensing, and super-resolution micro-endoscopy.

This work introduces an in-situ nano-displacement measurement system via a multimode fiber probe with superoscillatory speckles and deep learning. It achieves 10 nm resolution and 99.95% accuracy for wafers, non-contact sensing in confined spaces.

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