Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.23187 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- Measurements of microscale surface patterns are essential for process and quality control in industries across semiconductors, micro-machining, and biomedicines. However, the development of miniaturized and intelligent profiling systems remains a longstanding challenge, primarily due to the complexity and bulkiness of existing benchtop systems required to scan large-area samples. A real-time, in-situ, and fast detection alternative is therefore highly desirable for predicting surface topography on the fly. In this paper, we present an ultracompact geometric profiler based on photonic integrated circuits, which directly encodes the optical reflectance of the sample and decodes it with a neural network. This platform is free of complex interferometric configurations and avoids time-consuming nonlinear fitting algorithms. We show that a silicon programmable circuit can generate pseudo-random kernels to project input data into higher dimensions, enabling efficient feature extraction via a lightweight one-dimensional convolutional neural network. Our device is capable of high-fidelity, fast-scanning-rate thickness identification for both smoothly varying samples and intricate 3D printed emblem structures, paving the way for a new class of compact geometric sensors.