Expanding detection bandwidth via a photonic reservoir for ultrafast optical sensing

TL;DR

The paper addresses the fundamental limit of detector bandwidth in ultrafast optical sensing by introducing a photonic reservoir computing framework that uses spatiotemporal encoding to convert high-frequency information into multiple narrowband reservoir channels. A linear readout reconstructs broadband signals from these channels, enabling effective bandwidth expansion beyond the intrinsic detector limits. Experimentally, an on-chip silicon photonic reservoir demonstrates more than an eightfold expansion, with additional gains demonstrated via WDM. This approach offers a scalable, silicon-compatible path to ultrafast sensing and communications, while enabling phase-sensitive measurements and robustness to detector nonlinearities.

Abstract

The detection of ultrafast optical and radio-frequency (RF) signals is crucial for applications ranging from high-speed communications to advanced sensing. However, conventional detectors are fundamentally constrained by their intrinsic bandwidth, limiting accurate broadband signal measurement. Here, we show that a neuromorphic photonic processing approach can overcome this limitation, enabling accurate broadband signal detection beyond the detector bandwidth. The key idea lies in the spatiotemporal encoding of input waveforms within a photonic reservoir network, which reconstructs high-frequency components otherwise inaccessible to individual detectors. We experimentally demonstrate the detection of high-speed optical phase signals with more than an eightfold effective bandwidth expansion using an on-chip silicon photonic reservoir. This approach provides a scalable and integrable platform for high-speed optical and RF signal processing, opening new opportunities in ultrafast photonics and next-generation communication systems.

Figures (7)

• Figure 1: (a) High-speed signal detection using a single sensor whose bandwidth $B_{\rm sensor}$ is narrower than the signal bandwidth $B_{\rm sig}$. (b) Conceptual schematic of the proposed sensing approach and its photonic implementation. PDs: photodetectors.
• Figure 2: Numerical reconstruction results. (a) Original and reconstructed signals in the time domain. (b) Enlarged view of (a). (c) Power spectra of the original, sensor output, and reconstructed signals. (d) NMSE versus $N$ for different bandwidth ratios $B = B_{\rm sig}/B_{\rm sensor}$ for $L=3$. (e) NMSE versus $N$ for different training sample sizes $L$ for $B = 1000$.
• Figure 3: NMSE as a function of the SNR.
• Figure 4: (a)Photograph of the silicon photonic reservoir chip. (b)Schematic of a virtual reservoir network.
• Figure 5: Experimental bandwidth expansion demonstration. (a) Schematic of experimental setup. AWG: Arbitrary Waveform Generator; PM: Phase Modulator; EDFA: Erbium-Doped Fiber Amplifier; PD: Photodetector; ADC: Analog-Digital Converter. (b) Time-domain comparison between the original (target), filtered, and reconstructed signals for a 10-GHz test signal with a 1-GHz filter. Bandwidth ratio $B = 10$. (c) Power spectra. (d) NMSE versus $N$. (e) NMSE versus filter order.