Search

Current wireless communication systems are increasingly constrained by insufficient bandwidth and limited power output, impeding the achievement of ultra-high-speed data transmission. The terahertz (THz) range offers greater bandwidth, but it also imposes higher requirements on broadband and high-power devices. In this work, we present a modified uni-traveling-carrier photodiode (MUTC-PD) module with WR-6 waveguide output for photonics-assisted fiber-THz integrated wireless communications. Through the optimization of the epitaxial structure and high-impedance coplanar waveguide (CPW), the fabricated 6-um-diameter MUTC-PD achieves a high output power of -0.96 dBm at 150 GHz and ultra-flat frequency response at D-band. The MUTC-PD is subsequently packaged into a compact WR-6 module, incorporating planar-circuit-based RF-choke, DC-block and probe. The packaged PD module demonstrates high saturation power and flat frequency responses with minimal power roll-off of only 2 dB over 110-170 GHz. By incorporating the PD module into a fiber-THz integrated communication system, high data rates of up to 160 Gbps with 16 quadrature amplitude modulation (QAM) and a maximum symbol transmission rate of 60 Gbaud with QPSK modulation are successfully secured. The demonstration verifies the potential of the PD module for ultra-broadband and ultra-high-speed THz communications, setting a foundation for future research in high-speed data transmission.

The escalating demands of compute-intensive applications, including artificial intelligence, urgently necessitate the adoption of sophisticated optical on-chip interconnect technologies to overcome critical bottlenecks in scaling future computing systems. This transition requires leveraging the inherent parallelism of wavelength and mode dimensions of light, complemented by high-order modulation formats, to significantly enhance data throughput. Here we experimentally demonstrate a novel synergy of these three dimensions, achieving multi-tens-of-terabits-per-second on-chip interconnects using ultra-broadband, multi-mode digital metamaterials. Employing a highly efficient edge-guided analog-and-digital optimization method, we inversely design foundry-compatible, robust, and multi-port digital metamaterials with an 8xhigher computational efficiency. Using a packaged five-mode multiplexing chip, we demonstrate a single-wavelength interconnect capacity of 1.62 Tbit s-1 and a record-setting multi-dimensional interconnect capacity of 38.2 Tbit s-1 across 5 modes and 88 wavelength channels. A theoretical analysis suggests that further system optimization can enable on-chip interconnects to reach sub-petabit-per-second data transmission rates. This study highlights the transformative potential of optical interconnect technologies to surmount the constraints of electronic links, thus setting the stage for next-generation datacenter and optical compute interconnects.

In task-oriented dialogue scenarios, cross-domain zero-shot slot filling plays a vital role in leveraging source domain knowledge to learn a model with high generalization ability in unknown target domain where annotated data is unavailable. However, the existing state-of-the-art zero-shot slot filling methods have limited generalization ability in target domain, they only show effective knowledge transfer on seen slots and perform poorly on unseen slots. To alleviate this issue, we present a novel Hierarchical Contrastive Learning Framework (HiCL) for zero-shot slot filling. Specifically, we propose a coarse- to fine-grained contrastive learning based on Gaussian-distributed embedding to learn the generalized deep semantic relations between utterance-tokens, by optimizing inter- and intra-token distribution distance. This encourages HiCL to generalize to the slot types unseen at training phase. Furthermore, we present a new iterative label set semantics inference method to unbiasedly and separately evaluate the performance of unseen slot types which entangled with their counterparts (i.e., seen slot types) in the previous zero-shot slot filling evaluation methods. The extensive empirical experiments on four datasets demonstrate that the proposed method achieves comparable or even better performance than the current state-of-the-art zero-shot slot filling approaches., Comment: Accepted by Findings of EMNLP2023 as a long paper

We propose and study an efficient photonic CDE method using MZI-based coherent optical matrix multiplication. It improves the compensation performance by about 60% when the tap-length is limited, and only 50% taps of the theoretical value is needed for photonic CDE with 1-dB penalty., Comment: 4 pages,5 figures, Conference paper, submitted to European Conference on Optical Communication (ECOC)

Silicon microring modulator (Si-MRM) has become one of the most promising compact modulators to meet the increasing capacity requirements of the next generation optical interconnection. The limited electro-optical (E-O) bandwidth, low modulation efficiency, and inherent modulation nonlinearity are the major factors that limit the Si-MRM modulation speed. To address these issues, we comprehensively optimize the Si-MRM from the device to the modulation and the signal processing. Large modulation bandwidth over 67GHz is achieved in our newly fabricated Si-MRM. Additionally, the laser wavelength and bias voltage of Si-MRM are optimized to significantly improve the modulation performance. Finally, we comprehensively study the theoretical model of modulation nonlinearity in Si-MRM, especially transient nonlinearity. A bidirectional gate recurrent unit (Bi-GRU) neural network with minor modification is applied to compensate for the nonlinear impairments. With all these efforts, we experimentally demonstrate a 302 Gbps Si-MRM-based O-band optical interconnection and achieve 300 Gbps transmission over a 1-km standard single-mode fiber using the discrete multitone modulation format with bit and power loading (BPL-DMT)., Comment: 15 pages,7 figures, Journal paper, submitted to Nature Communication

A novel intelligent bandwidth allocation scheme in NG-EPON using reinforcement learning is proposed and demonstrated for latency management. We verify the capability of the proposed scheme under both fixed and dynamic traffic loads scenarios to achieve <1ms average latency. The RL agent demonstrates an efficient intelligent mechanism to manage the latency, which provides a promising IBA solution for the next-generation access network.