Currently, the global 5G network, cloud computing, and data center industries are experiencing rapid development. The continuous growth of data center traffic has driven the vigorous progress in high-speed optical transceivers for optical interconnection within data centers. The electro-absorption modulated laser (EML), which is widely used in optical fiber communications, data centers, and high-speed data transmission systems, represents a high-performance photoelectric conversion device. Compared to traditional directly modulated lasers (DMLs), EMLs demonstrate lower frequency chirp and higher modulation bandwidth, enabling support for higher data rates and longer transmission distances. This article introduces the composition, working principles, manufacturing processes, and applications of EMLs. It reviews the progress on advanced indium phosphide (InP)-based EML devices from research institutions worldwide, while summarizing and comparing data transmission rates and key technical approaches across various studies.

Accurate and quantitative evaluation of human fatigue status is of crucial importance to safe outdoor operations and personal health management. Electroencephalography (EEG) technology offers a non-invasive, rapid and high-accuracy feasible solution, yet it still faces challenges such as large device volume and unstable electrode-skin interface. In this work, we propose a wearable intelligent EEG platform for real-time monitoring and assessment of human fatigue status. A flexible dual-channel (FP1, FP2) EEG patch was fabricated in flexible PET film by coupling screen-printed carbon powder/graphene oxide electrodes with a biocompatible polyacrylic acid/polyvinyl alcohol (PAA/PVA) hydrogel. Among them, the composite carbon structure and the hydrogel provide a low interfacial impedance (98.3 Ω·cm²@1 kHz), skin-matched mechanical modulus (3.5 kPa) and a skin-conformal (296 kPa adhesion strength) electronic interface, respectively, laying a solid foundation for acquiring high-quality and stable EEG signals. Furthermore, a smartphone APP was developed to wirelessly operate the EEG platform, as well as to transmit and process real-time EEG data. To verify the effectiveness, a multi-state simulation-induced fatigue test was conducted. The results demonstrate that the proposed EEG platform can detect the EEG spectrum and conduct rhythmic classification processing, in which the θ/β value (>1.5) could serve as a reliable indicator of fatigue, enabling quantitative evaluation and early warning of human fatigue.

The computational cost of TCAD simulations is becoming prohibitively high with the complexity of advanced process technologies, making simulation acceleration a critical research priority. While end-to-end surrogate models mapping process recipes to device structures and characteristics offer a promising alternative, their application is often limited by poor generalizability and explainability. In this work, we present MPNet, a modular deep learning surrogate modeling framework for process TCAD. MPNet comprises distinct surrogate models for individual process modules, which are assembled into an integrated framework. These modular models employ a novel UNet-attention feature evolution method to capture the complex evolutions of device geometry and doping profiles. Each module can be trained separately on its individual process, after which the modules are cascaded and jointly fine-tuned to minimize error accumulation throughout the cascade. The efficacy of the proposed MPNet framework is demonstrated through a MOSFET integrated process TCAD case study. Results show that MPNet achieves a computational speedup of over 10³ times compared to conventional TCAD, while maintaining predictive fidelity exceeding 98%. Finally, to illustrated the application of the proposed framework, MPNet is coupled with a PSO algorithm, showcasing its utility for fast process optimization to meet specific process targets.

In DSP-based SerDes application, it is essential for AFE to implement a pre-ADC equalization to provide a better signal for ADC and DSP. To meet the various equalization requirements of different channel and transmitter configurations, this paper presents a 112 Gbps DSP-Based PAM4 SerDes receiver with a wide band equalization tuning AFE. The AFE is realized by implementing source degeneration transconductance, feedforward high-pass branch and inductive feedback peaking TIA. The AFE offers a flexible equalization gain tuning of up to 17.5 dB at Nyquist frequency without affecting the DC gain. With the proposed AFE, the receiver demonstrates eye opening after digital FIR equalization and achieves 6 × 10⁻⁹ BER with a 29.6 dB insertion loss channel.

Electromyography (EMG) is widely used in sports rehabilitation to evaluate muscle activation, coordination, fatigue, and functional recovery, yet reliable recording remains limited by the electrode-skin interface during repeated motion and prolonged wear. Microneedle electrodes offer a distinct interface strategy by penetrating the stratum corneum and forming lower-impedance, more stable electrical contact than conventional wet or dry electrodes. This review discusses microneedle electrodes for EMG acquisition in sports rehabilitation from a design-to-application perspective. We first clarify the interface requirements of EMG recording in rehabilitation settings and the technical rationale for using microneedle interfaces. We then summarize material and structural design strategies in silicon-, polymer-, and metal-based systems, focusing on how they balance penetration capability, mechanical compliance, stretchability, conductivity, and recording stability. Fabrication routes are further examined in terms of material-structure-process coupling, followed by applications in static assessment, dynamic motion monitoring, and clinical rehabilitation evaluation. This review integrates interface requirements, design, manufacturing, and validation to provide a structured overview of microneedle EMG electrodes' capabilities and limitations for sports rehabilitation.

Organic electrochemical transistors (OECTs), leveraged by their unique volumetric doping mechanism and ultra-high transconductance performance, have emerged as a pivotal device platform for constructing high-performance bioelectronic interfaces. OECT-based theranostics aim to develop intelligent closed-loop systems that integrate sensing, decision-making, and execution, thereby overcoming the latency and discretization limitations of traditional medical models when managing dynamic physiological fluctuations. This article systematically reviews the performance evolution of OECT materials from p-type to n-type, discusses critical strategies for enhancing device stability and transconductance density, such as side-chain engineering and ladder-type molecular design, while emphasizing the essential role of complementary logic circuits in minimizing the static power consumption of implantable electronics. Furthermore, breakthroughs in OECT-based neuromorphic computing are addressed; by simulating synaptic plasticity (STP/LTP) and engineering organic electrochemical neurons (OECNs), a highly efficient sensing-computing closed-loop architecture has been realized. The current application landscape of OECTs in electrophysiological monitoring, neurochemical sensing, and multimodal synergistic sensing is detailed, alongside a summary of high-density array fabrication and system integration strategies, including 3D printing, inkjet printing, and 3D hydrogel integration. Finally, future outlooks are provided, focusing on challenges such as the environmental stability of n-type materials, multimodal signal crosstalk, and long-term clinical reliability.

Micro-scale light-emitting diodes (Micro-LEDs) are evolving from display devices to powerful biomedical tools owing to their miniaturization, low power consumption, flexibility and multi-wavelength capability. This review summarizes advances in Micro-LED biomedicine (2024−2026). It introduces core enabling technologies and classifies applications into implantable systems, wearable platforms and brain−computer interfaces, covering optogenetics, cardiac/tumor therapy, epidermal healthcare, visual prostheses and all-optical neural interfaces. Key challenges including biocompatibility, heat dissipation and packaging are discussed. Prospects such as closed-loop intelligent systems, degradable devices and clinical translation are proposed. This review highlights Micro-LEDs’ great potential for next-generation precision medicine.