Endalamaw Ewnu Kassa | Searcharxiv

arXiv2026

Bias-Modulated Electronically-Tunable Optoelectronic Chromatic Dispersion in Ge PN Photodiodes

Optoelectronic Chromatic Dispersion (OED) is a source of significant wavelength-dependent current in photodiodes, offering a pathway to computational spectroscopy in a photodiode-only platform without the need for additional optical or mechanical components. Previous studies have activated OED through light modulation. Here, we present a theoretical and experimental investigation of bias-modulated OED in the C- and L-bands in a Ge PN photodiode, where the applied bias voltage is modulated sinusoidally, and the optical illumination is held constant. The spectral response is due to wavelength-dependent optical absorption together with bias-dependent competition between carrier drift, displacement current, and mobile-charge screening. With the RF phase-shift technique, the maximum measured OED sensitivity is +4.13 deg/nm in the L-band, significantly larger than that of light-modulated OED in Ge. The 15 mdeg phase-noise floor yields a spectral resolution of 3.6 pm. These results establish bm-OED as an electronically reconfigurable dispersive mechanism for applications in chip-scale computational spectrometers, photonic integrated circuits (PICs), on-chip RF photonics, and wavelength-division multiplexing.

physics.optics↗

arXiv2026

Optoelectronic Chromatic Dispersion in a Single Photodiode for Machine-Learning-Based Computational Spectroscopy

Spectroscopy requires high-precision wavelength discrimination but typically requires bulky, alignment-sensitive instrumentation. To address this, we present a compact computational spectrometer built from a single germanium PN photodiode. The system exploits optoelectronic chromatic dispersion (OED), a phenomenon whereby wavelength-dependent absorption depth produces carrier diffusion delays that encode spectral information as measurable RF amplitude and phase signatures in the photodiode output. We extract DC voltage, RF amplitude, and RF phase across 15 modulation frequencies (0.1-1.5 MHz), forming a 31-dimensional feature vector per optical input. Spectral reconstruction was formulated as a high-dimensional inverse problem and solved using five machine learning models, utilizing group-wavelength splitting and k-fold cross-validation to prevent spectral leakage and ensure unbiased evaluation. Across the C- and L-bands, single-wavelength reconstruction using Gaussian Process Regression (GPR) achieves an accuracy of 0.178 nm on a wavelength-grouped, held-out test set spanning seven optical power levels. Five-fold cross-validation yields a robust Root Mean Square Error (RMSE) of (0.342 +/- 0.117) nm, confirming excellent generalization under wavelength and power variations. For dual-wavelength inputs, GPR yields accuracies of 0.362 nm for the swept wavelength and 0.434 nm for the fixed wavelength. This is the first spectral reconstruction method exploiting a multi-frequency OED feature space from a single photodiode. By merging the physics of OED with data-driven learning, this work enables alignment-free, on-chip-compatible spectrometers suitable for portable optical sensing, smartphone-integrated diagnostics, and field-deployable environmental monitoring.

physics.optics↗