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.

Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algorithms and supporting hardware. In recent years, the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models. This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance. Therefore, algorithm-hardware co-design has emerged as the primary methodology, which analyzes algorithm behaviors on hardware to identify common computational properties. These properties can motivate algorithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency. We then reviewed recent works on robotic and embodied AI algorithms and computing hardware to demonstrate this algorithm-hardware co-design methodology. In the end, we discuss future research opportunities by answering two questions: (1) how to adapt the computing platforms to the rapid evolution of embodied AI algorithms, and (2) how to transform the potential of emerging hardware innovations into end-to-end inference improvements.

The unique structure and exceptional properties of two-dimensional (2D) materials offer significant potential for transformative advancements in semiconductor industry. Similar to the reliance on wafer-scale single-crystal ingots for silicon-based chips, practical applications of 2D materials at the chip level needs large-scale, high-quality production of 2D single crystals. Over the past two decades, the size of 2D single-crystals has been improved to wafer or meter scale, where the nucleation control during the growth process is particularly important. Therefore, it is essential to conduct a comprehensive review of nucleation control in 2D materials to gain fundamental insights into the growth of 2D single-crystal materials. This review mainly focuses on two aspects: controlling nucleation density to enable the growth from a single nucleus, and controlling nucleation position to achieve the unidirectionally aligned islands and subsequent seamless stitching. Finally, we provide an overview and forecast of the strategic pathways for emerging 2D materials.

Infrared and terahertz waves constitute pivotal bands within the electromagnetic spectrum, distinguished by their robust penetration capabilities and non-ionizing nature. These wavebands offer the potential for achieving high-resolution and non-destructive detection methodologies, thereby possessing considerable research significance across diverse domains including communication technologies, biomedical applications, and security screening systems. Two-dimensional materials, owing to their distinctive optoelectronic attributes, have found widespread application in photodetection endeavors. Nonetheless, their efficacy diminishes when tasked with detecting lower photon energies. Furthermore, as the landscape of device integration evolves, two-dimensional materials struggle to align with the stringent demands for device superior performance. Topological materials, with their topologically protected electronic states and non-trivial topological invariants, exhibit quantum anomalous Hall effects and ultra-high carrier mobility, providing a new approach for seeking photosensitive materials for infrared and terahertz photodetectors. This article introduces various types of topological materials and their properties, followed by an explanation of the detection mechanism and performance parameters of photodetectors. Finally, it summarizes the current research status of near-infrared to far-infrared photodetectors and terahertz photodetectors based on topological materials, discussing the challenges faced and future prospects in their development.

Graphene has garnered significant attention in photodetection due to its exceptional optical, electrical, mechanical, and thermal properties. However, the practical application of two-dimensional (2D) graphene in optoelectronic fields is limited by its weak light absorption (only 2.3%) and zero bandgap characteristics. Increasing light absorption is a critical scientific challenge for developing high-performance graphene-based photodetectors. Three-dimensional (3D) graphene comprises vertically grown stacked 2D-graphene layers and features a distinctive porous structure. Unlike 2D-graphene, 3D-graphene offers a larger specific surface area, improved electrochemical activity, and high chemical stability, making it a promising material for optoelectronic detection. Importantly, 3D-graphene has an optical microcavity structure that enhances light absorption through interaction with incoming light. This paper systematically reviews and analyzes the current research status and challenges of 3D-graphene-based photodetectors, aiming to explore feasible development paths for these devices and promote their industrial application.

In recent years, as the dimensions of the conventional semiconductor technology is approaching the physical limits, while the multifunction circuits are restricted by the relatively fixed characteristics of the traditional metal−oxide−semiconductor field-effect transistors, reconfigurable devices that can realize reconfigurable characteristics and multiple functions at device level have been seen as a promising method to improve integration density and reduce power consumption. Owing to the ultrathin structure, effective control of the electronic characteristics and ability to modulate structural defects, two-dimensional (2D) materials have been widely used to fabricate reconfigurable devices. In this review, we summarize the working principles and related logic applications of reconfigurable devices based on 2D materials, including generating tunable anti-ambipolar responses and demonstrating nonvolatile operations. Furthermore, we discuss the analog signal processing applications of anti-ambipolar transistors and the artificial intelligence hardware implementations based on reconfigurable transistors and memristors, respectively, therefore highlighting the outstanding advantages of reconfigurable devices in footprint, energy consumption and performance. Finally, we discuss the challenges of the 2D materials-based reconfigurable devices.

As traditional von Neumann architectures face limitations in handling the demands of big data and complex computational tasks, neuromorphic computing has emerged as a promising alternative, inspired by the human brain's neural networks. Volatile memristors, particularly Mott and diffusive memristors, have garnered significant attention for their ability to emulate neuronal dynamics, such as spiking and firing patterns, enabling the development of reconfigurable and adaptive computing systems. Recent advancements include the implementation of leaky integrate-and-fire neurons, Hodgkin−Huxley neurons, optoelectronic neurons, and time-surface neurons, all utilizing volatile memristors to achieve efficient, low-power, and highly integrated neuromorphic systems. This paper reviews the latest progress in volatile memristor-based artificial neurons, highlighting their potential for energy-efficient computing and integration with artificial synapses. We conclude by addressing challenges such as improving memristor reliability and exploring new architectures to advance memristor-based neuromorphic computing.

The novel HfO₂-based ferroelectric field effect transistor (FeFET) is considered a promising candidate for next-generation nonvolatile memory (NVM). However, a series of reliability issues caused by the fatigue effect hinder its further development. Therefore, a comprehensive understanding of the fatigue mechanisms of the device and optimization strategies is essential for its application. The fundamental mechanism of the fatigue effect is attributed to charge trapping and trap generation based on the current studies, and the underlying causes, occurrence locations and specific impacts are analyzed in this review. In particular, the asymmetric trapping/detrapping of electrons and holes, as well as the relationship between the ferroelectric (FE) polarization and charge trapping, are given particular attention. After categorizing and summarizing the current progress, we propose a series of optimization strategies derived based on the fatigue mechanisms.

Due to advantages of high power-conversion efficiency (PCE), large power-to-weight ratio (PWR), low cost and solution processibility, flexible perovskite solar cells (f-PSCs) have attracted extensive attention in recent years. The PCE of f-PSCs has developed rapidly to over 25%, showing great application prospects in aerospace and wearable electronic devices. This review systematically sorts device structures and compositions of f-PSCs, summarizes various methods to improve its efficiency and stability recent years. In addition, the applications and potentials of f-PSCs in space vehicle and aircraft was discussed. At last, we prospect the key scientific and technological issues that need to be addressed for f-PSCs at current stage.

Integrated perovskite-organic solar cells (IPOSCs) offer a promising hybrid approach that combines the advantages of perovskite and organic solar cells, enabling efficient photon absorption across a broad spectrum with a simplified architecture. However, challenges such as limited charge mobility in organic bulk heterojunction (BHJ) layers, and energy-level mismatch at the perovskite/BHJ interface still sustain. Recent advancements in non-fullerene acceptors (NFAs), interfacial engineering, and emerging materials have improved charge transfer/transport, and overall power conversion efficiency (PCE) of IPOSCs. This review explores key developments in IPOSCs, focusing on low-bandgap materials for near-infrared absorption, energy alignment optimization, and strategies to enhance photocurrent density and device performance. Future innovations in material selection and device architecture will be crucial for further improving the efficiency of IPOSCs, bringing them closer to practical application in next-generation photovoltaic technologies.