Silicon modulators are key components to support the dense integration of electro-optic functional elements for various applications. Despite numerous advances in promoting the modulation speed, a bandwidth ceiling emerges in practices and becomes an obstacle toward Tbps-level throughput on a single chip. Here, we demonstrate a compact pure silicon modulator that shatters present bandwidth ceiling to 110 gigahertz. The proposed modulator is built on a cascade corrugated waveguide architecture, which gives rise to a slow-light effect. By comprehensively balancing a series of merits, the modulators can benefit from the slow light for better efficiency and compact size while remaining sufficiently high bandwidth. Consequently, we realize a 110-gigahertz modulator with 124-micrometer length, enabling 112 gigabits per second on-off keying operation. Our work proves that silicon modulators with 110 gigahertz are feasible, thus shedding light on its potentials in ultrahigh bandwidth applications such as optical interconnection and photonic machine learning.

A data-driven fiber channel modeling method based on deep learning (DL) is introduced in an optical communication system. In this study, bidirectional long short-term memory (BiLSTM) is selected from a diverse range of DL algorithms to perform fiber channel modeling for on—off keying and pulse amplitude modulation 4 signals. Compared with the conventional model-driven split-step Fourier (SSF)-based method, the proposed method yields similar results based on the comprehensive comparison of multiple characteristics associated with the generated optical signals, including the optical amplitude and phase waveforms in the time domain, optical spectrum components in the frequency domain, and eye diagrams after detection in the electrical domain. Additionally, the effects of multiple factors on the modeled fiber channel have also be investigated, including fiber length, fiber nonlinearity, dispersion, data pattern, pulse shaping, and sample rate. The satisfactory fitting results and acceptable mean square errors indicate that the approximate transfer function of the fiber channel is learned by the BiLSTM. Moreover, compared with repetitive iteration SSF, the computing time is significantly reduced by the BiLSTM owing to its independence on fiber length and insensitivity to data size and launch power. Our aim is to demonstrate the BiLSTM is comparable with the conventional model-driven SSF-based method for direct-detection optical fiber system. We think the proposed method could be a supplementary technique that can be used for the existing simulation system and could also be a potential option for future simulation methods.

The integrated microwave photonic filter (MPF), as a compelling candidate for next-generation radio-frequency (RF) applications, has been widely investigated for decades. However, most integrated MPFs reported thus far have merely incorporated passive photonic components onto a chip-scale platform, while all necessary active devices are still bulk and discrete. Though few attempts to higher photonic integration of MPFs have been executed, the achieved filtering performances are fairly limited, which impedes the pathway to practical deployments. Here, we demonstrate, for the first time to our knowledge, an all-integrated MPF combined with high filtering performances, through hybrid integration of an InP chip-based laser and a monolithic silicon photonic circuit consisting of a dual-drive Mach–Zehnder modulator, a high- Q ring resonator, and a photodetector. This integrated MPF exhibits a high spectral resolution as narrow as 360 MHz, a wide-frequency tunable range covering the S-band to K-band (3 to 25 GHz), and a large rejection ratio of > 40 dB . Moreover, the filtering response can be agilely switched between the bandpass and band-stop function with a transient respond time ( ∼ 48 μs ). Compared with previous MPFs in a similar integration level, the obtained spectral resolution in this work is dramatically improved by nearly one order of magnitude, while the valid frequency tunable range is broadened more than twice, which can satisfy the essential filtering requirements in actual RF systems. As a paradigm demonstration oriented to real-world scenarios, high-resolution RF filtering of realistic microwave signals aiming for interference rejection and channel selection is performed. Our work points out a feasible route to a miniaturized, high-performance, and cost-effective MPF leveraging hybrid integration approach, thus enabling a range of RF applications from wireless communication to radar toward the higher-frequency region, more compact size, and lower power consumption.

Integrated microwave photonic filters (IMPFs) are capable of offering unparalleled performances in terms of superb spectral fineness, broadband, and more importantly, the reconfigurability, which encounter the trend of the next-generation wireless communication. However, to achieve high reconfigurability, previous works should adopt complicated system structures and modulation formats, which put great pressure on power consumption and controlment, and, therefore, impede the massive deployment of IMPF. Here, we propose a streamlined architecture for a wideband and highly reconfigurable IMPF on the silicon photonics platform. For various practical filter responses, to avoid complex auxiliary devices and bias drift problems, a phase-modulated flexible sideband cancellation method is employed based on the intensity-consistent single-stage-adjustable cascaded-microring (ICSSA-CM). The IMPF exhibits an operation band extending to millimeter-wave (≥30 GHz), and other extraordinary performances including high spectral resolution of 220 MHz and large rejection ratio of 60 dB are obtained. Moreover, Gb/s-level RF wireless communications are demonstrated for the first time towards real-world scenarios. The proposed IMPF provides broadband flexible spectrum control capabilities, showing great potential in the next-generation wireless communication.

Nonlinear optics-based optical signal processing (OSP) could potentially increase network flexibility because of its transparency, tunability, and large bandwidth. A low-loss, high nonlinearity, and compact integrated material platform is always the pursuit of OSP. In this Letter, a high-efficiency, one-to-six wavelength multicasting of 10 Gbaud pulse-amplitude modulation (PAM3/PAM4) signals using a 6 cm long A l 0.2 G a 0.8 A s -on-insulator nanowaveguide is experimentally demonstrated for the first time, to the best of our knowledge. The low-loss, combined with the high nonlinear coefficient of the AlGaAsOI platform, enables us to achieve − 11.2 d B average conversion efficiency clear eye diagrams and < 2.1 d B power penalty at KP4-forward error correction threshold ( 2.4 × 10 − 4 ) for all the output PAM3/PAM4 multicasting channels. This result points to a new generation of nonlinear OSP photonic integrated circuits.

Nanophotonic devices have enabled electromagnetic manipulation with unprecedented accuracy and functionalities. With the increased functionality requirements of such devices and the progress of fabrication technologies, trends toward miniaturization and multi-functionality have emerged, making forward response prediction and inverse design increasingly complicated. In this paper, a physical information-embedded deep learning model for flexible forward prediction and multifunctional inverse design is proposed. Besides the overwhelming advantage of computing efficiency over conventional iterative optimization methods, the proposed method exhibits strong generalization on different wavelengths and polarizations by embedding the corresponding physical information into the model through effective sparse parameter method (SPM). For the forward prediction, responses can be predicted among the 1300-1550 nm wavelength band and two polarizations (TM/TE) with high accuracy. For the inverse design, with the assistance of forward prediction models and broadband training methods, multi-dimensional information for the target can be considered simultaneously to retrieve the satisfactory desired designs topologies in an unsupervised way. The multi-functionality of inverse design is demonstrated by implementing wavelength demultiplexers with small crosstalk and power splitters with arbitrary splitting ratios on a 250 nm broadband wavelength in a compact footprint of 2 × 2 μm ² without additional heavy training of the model.