Electrically Reconfigurable Non-Volatile On-Chip Bragg Filter with Multilevel Operation

Photonic integrated circuits (PICs) demand tailored spectral responses for various applications. On-chip Bragg filters offer a promising solution, yet their static nature hampers scalability. Current tunable filters rely on volatile switching mechanisms plagued by high static power consumption and thermal crosstalk. Here, we introduce, for the first time, a non-volatile, electrically programmable on-chip Bragg filter. This device incorporates a nanoscale layer of wide-bandgap phase change material (Sb2S3) atop a periodically structured silicon waveguide. The reversible phase transitions and drastic refractive index modulation of Sb2S3 enable dynamic spectral tuning via foundry-compatible microheaters. Our design surpasses traditional passive Bragg gratings and active volatile filters by offering electrically controlled, reconfigurable spectral responses in a non-volatile manner. The proposed filter achieves a peak reflectivity exceeding 99% and a high tuning range ($\Delta\lambda$=20 nm) when transitioning between the amorphous and crystalline states of Sb2S3. Additionally, we demonstrate quasi-continuous spectral control of the filter stopband by modulating the amorphous/crystalline distribution within Sb2S3. Our approach offers substantial benefits for low-power, programmable PICs, thereby laying the groundwork for prospective applications in optical communications, optical interconnects, microwave photonics, optical signal processing, and adaptive multi-parameter sensing.

Information Processing in Hybrid Photonic Electrical Reservoir Computing

Physical Reservoir Computing (PRC) is a recently developed variant of Neuromorphic Computing, where a pertinent physical system effectively projects information encoded in the input signal into a higher-dimensional space. While various physical hardware has demonstrated promising results for Reservoir Computing (RC), systems allowing tunability of their dynamical regimes have not received much attention regarding how to optimize relevant system parameters. In this work we employ hybrid photonic-electronic (HPE) system offering both parallelism inherent to light propagation, and electronic memory and programmable feedback allowing to induce nonlinear dynamics and tunable encoding of the photonic signal to realize HPE-RC. Specifically, we experimentally and theoretically analyze performance of integrated silicon photonic on-chip Mach-Zehnder interferometer and ring resonators with heaters acting as programmable phase modulators, controlled by detector and the feedback unit capable of realizing complex temporal dynamics of the photonic signal. Furthermore, we present an algorithm capable of predicting optimal parameters for RC by analyzing the corresponding Lyapunov exponent of the output signal and mutual information of reservoir nodes. By implementing the derived optimal parameters, we demonstrate that the corresponding resulting error of RC can be lowered by several orders of magnitude compared to a reservoir operating with randomly chosen set of parameters.