arXiv subjects

Zhongming Huang

Publications and source records attributed to Zhongming Huang.

2 recordsLinked to original sources

arXiv2026

Calibration-Free Broadband Reconstructive Spectrometer based on Conical Phase Modulation

Reconstructive spectrometers (RSs) shift spectral analysis from hardware-heavy dispersion to encoding-decoding schemes. In the speckle-based RSs, spectra are encoded into disordered intensity patterns and then recovered algorithmically. However, characterizing such spectral-encoding transmission matrices (TMs) typically demands empirically complex calibrations, which rely on expensive tunable light sources to generate known wavelengths. In this work, we demonstrate a fully calibration-free RS that leverages conical phase modulation from an axicon. The target spectra are encoded into a Bessel-form intensity profile, enabling computational construction of a deterministic TM and thus completely removing the need for optical calibration. With this computer-generated TM, broadband spectral reconstruction is experimentally achieved across a 650 nm range (1050-1700 nm)--nearly three times the bandwidth of existing calibration-simplified RSs--while a spectral resolution of 2.9 nm remains comparable to that of these counterparts. Compared with an RS requiring optical calibration for equivalent performance, our solution achieves an approximate 36-fold cost reduction.Unlike most speckle RSs, the axicon RS requires no fixed input polarizer, further enhancing energy efficiency and simplicity through polarization-insensitive operation. To the best of our knowledge, this is the first reported experimentally calibration-free RS, featuring superior simplicity, practicality, and cost-effectiveness for practical applications.

physics.optics↗

arXiv2024

Lensless speckle reconstructive spectrometer via physics-aware neural network

The speckle field yielded by disordered media is extensively employed for spectral measurements. Existing speckle reconstructive spectrometers (RSs) implemented by neural networks primarily rely on supervised learning, which necessitates large-scale spectra-speckle pairs. However, beyond system stability requirements for prolonged data collection, generating diverse spectra with high resolution and finely labeling them is particularly difficult. A lack of variety in datasets hinders the generalization of neural networks to new spectrum types. Here we avoid this limitation by introducing PhyspeNet, an untrained spectrum reconstruction framework combining a convolutional neural network (CNN) with a physical model of a chaotic optical cavity. Without pre-training and prior knowledge about the spectrum under test, PhyspeNet requires only a single captured speckle for various multi-wavelength reconstruction tasks. Experimentally, we demonstrate a lens-free, snapshot RS system by leveraging the one-to-many mapping between spatial and spectrum domains in a random medium. Dual-wavelength peaks separated by 2 pm can be distinguished, and a maximum working bandwidth of 40 nm is achieved with high measurement accuracy. This approach establishes a new paradigm for neural network-based RS systems, entirely eliminating reliance on datasets while ensuring that computational results exhibit a high degree of generalizability and physical explainability.

physics.optics↗