He Zhu

Miniaturized spectrometers are essential for portable and consumer-oriented applications, yet their reduced physical footprint inherently limits the detector’s active area, leading to compromised sensitivity. Conventional strategies to enhance sensitivity, such as cryogenic cooling or photomultiplier tubes, are incompatible with miniaturization due to their bulk, cost, and power demands. Here, we present a miniaturized spectrometer based on a van der Waals heterojunction integrated on a ferroelectric thin film. Spectral discrimination is enabled by engineering both interlayer and intralayer photoresponses within the heterojunction, which can be dynamically tuned solely via the source-drain bias, eliminating the need for a gate electrode and enabling a two-terminal architecture. This design is inherently compatible with simple two-pad readout integrated circuits, facilitating scalable array integration for compact spectroscopic systems. Critically, the built-in electric field generated by the ferroelectric polarization promotes efficient dissociation of interlayer excitons, yielding an ultralow noise floor of below 5 × 10–16 A Hz–1/2, more than 2 orders of magnitude lower than existing miniaturized spectrometers and surpassing the performance of most benchtop instruments. This work in sensitivity without sacrificing size or complexity present a strategy for deploying high-performance spectroscopy in real-world applications.

Photodetectors capable of simultaneously capturing the intensity and temporal information of incident light are highly pursued by intelligent perception technology. However, existing state‐of‐the‐art multi‐dimensional intensity‐time photodetectors (ITPDs), primarily based on charge‐trapping or pyroelectric effects, suffer from limited sensitivity and speed, which restricts their applications in high‐grade fields. Here, we propose an ITPD featuring ultrahigh sensitivity and high‐speed operation by integrating photoelectric and electrocaloric effects within a van der Waals heterojunction and a spontaneously polarized dielectric film, respectively. The dielectric surface induces a positive charge layer, enabling bias‐free photocurrent via nonequilibrium charge transfer, while the electrocaloric effect facilitates rapid current quenching. Consequently, the proposed ITPD presents a spike response, with a peak cusp detectivity of 5 × 10 ¹³ Jones, surpassing current benchmarks by an order of magnitude, while reaching a record frame rate ( f _fr , i.e., 1/ τ _r ) of 0.25 MHz. This work provides a robust strategy for high‐performance ITPDs, offering broad prospects in high‐capacity optical communication and real‐time information processing.

The difficulty of growing high-quality infrared-sensitive materials and their heterogeneous integration with silicon circuits has long been a key factor hindering the development of infrared detectors. In this work, we report a broadband infrared detector fabricated directly on an SOI substrate through arsenic doping. The optical absorption efficiency in the absorption region (AR) is greatly enhanced through a sophisticated photo-trapping structure design, leading to a significant improvement in quantum efficiency over the entire response range. The fabricated photo-trapping SOI:As detectors exhibit average enhancement of 346% for external quantum efficiency (EQE) and 696% for internal quantum efficiency (IQE) across the 7–22 μm range. The EQE and the IQE at the peak wavelength reach 20% and 40% with an extremely thin AR (~100 nm). This CMOS-compatible hybrid architecture provides a promising pathway toward high-performance and large-scale monolithic infrared detector arrays.

Terahertz (THz) technology has been widely used in astronomical observation where blocked impurity band (BIB) terahertz detectors are key components for acquiring THz signals in space scenario. In this study we proposed the integration of a plasmonic micro-nano structure onto Ge:Ga BIB detector and successfully realized the enhancement of the THz response performance. The BIB detector integrated with plasmonic structure shows a 64.5% improvement in blackbody responsivity under 4 K and 300 mV bias. The peak detectivity and noise equivalent power (NEP) are also significantly improved, 7.0×10¹⁴ Jones and 7.0×10^-17 W/Hz^1/2, respectively. It is revealed that hot-electron effect arisen from the plasmonic excitation plays key role in the detector performance improvement. This work provides a new perspective for the development of high-sensitivity THz detectors.

Mid-infrared spectrum is a critical tool for chemical analysis, industrial inspection, environment, and other fields due to its rich chemical bond information. However, the complicated growth or fabrication procedures of existing mid-infrared sensitive materials hinder the large-scale production and utilization of mid-infrared detectors. To address this issue, we developed Si:S barrier detectors employing sulfur doped silicon and a sophisticated band barrier design. Since the transport of dark current and photo current is separated, the barrier design effectively suppresses the dark current while allowing the photo current to leverage gain mechanisms, thereby substantially improving signal-to-noise ratio. As a result, the detector exhibits an infrared response range covering from 1.12 to 4.4μm with a peak at 3.3μm, excluding its intrinsic response in visible range. Its peak quantum efficiency surpasses that of the best mid-infrared silicon-based detector reported to date by an order of magnitude, reaching 2% at room temperature. The peak detectivity at 90K is 1.4E11 Jones @1.4V and decreases to 4.4E9 Jones @1.4V, 210K, comparable to the typical III-V and IV-VI photodetectors at one thousandth fabrication cost. Leveraging the well-established silicon-based manufacturing process, this device holds promise for large-scale production at a reduced price, offering a cost-effective solution for future mid-infrared detection.

A low-loss seven-core ring core fiber with a special inner-cladding refractive index is introduced and tested, which exhibits excellent comprehensive performance in terms of attenuation and crosstalk among mode groups.

Intelligent systems have brought convenience to contemporary society. However, latency and poor-efficiency have been urgent problems for intelligence systems. Here, an infrared (IR) intelligent system fusing a non-contact IR thermopile sensor array fabricated by microelectromechanical system technology and an artificial neural network (ANN) algorithm is proposed, with the characteristics of high-efficiency and low-latency. The system is based on a designed near-sensor computing architecture, which can realize computing directly on edge devices without sending data to the cloud, resulting in reduced redundant data and decreased latency. The responsivity of the sensors affects the weight accuracy and computing speed of the ANN algorithm and further influences the accuracy and efficiency of the system. Transfer of graphene oxide (GO) material to the IR thermopile sensors with a proposed location transfer method is suggested to enhance the responsivity. The responsivity of the IR thermopiles with GO is up to 705.1 V/W, which is enhanced by 85.9% compared to that without GO. The system is applied to gesture recognition to study practicality. The recognition accuracy of the system with GO is 100%. This work provides an effective idea for studying a high-accuracy IR intelligent sensing system.

The range resolution of high-resolution wideband radar is much smaller than the target size. Its echo signals tend to be diverse and sensitive to small changes of targets. Therefore, it is difficult to capture and distinguish the features in radar signals. In this article, we propose a radar target recognition pipeline based on a deep nested neural network. The framework consists of three parts: The translation sensitivity of the training data is first addressed in the preprocessing section. The second step is to obtain an embedded representation of the radar echo signals by the combination of the adjustment layer, convolutional neural network (CNN), and the squeeze and excitation (SE) block. Finally, the target is recognized through inputting embedded representation as a time sequence into the stacked bidirectional recurrent neural network (bi-RNN) based on an attention mechanism. Compared with the traditional methods, the proposed deep nested neural network extracts and takes advantage of the features of radar echo signals more effectively, including the envelope features and local physical structural features. The experimental results based on the test data indicate that the proposed method has a great advantage over other methods in the case of large data sets as well as small training data sets and is robust to the small translation of test samples and noises, exhibiting high engineering practical value.

Unfortunately, we made serious mistakes with the information of the authors in [1] . The e-mail of Weida Hu should be wdhu@mail.sitp.ac.cn. The affiliation address of He Zhu and Weida Hu should be corrected as follows: