A two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding

Broadband mid-infrared photodetectors utilizing two-dimensional van der Waals heterostructures with parallel-stacked pn junctions

Optimizing the width of depletion region is a key consideration in designing high performance photovoltaic photodetectors, as the electron-hole pairs generated outside the depletion region cannot be effectively separated, leading to a negligible contribution to the overall photocurrent. However, currently reported photovoltaic mid-infrared photodetectors based on two-dimensional heterostructures usually adopt a single pn junction configuration, where the depletion region width is not maximally optimized. Here, we demonstrate the construction of a high performance broadband mid-infrared photodetector based on a MoS2/b-AsP/MoS2npn van der Waals heterostructure. The npn heterojunction can be equivalently represented as two parallel-stacked pn junctions, effectively increasing the thickness of the depletion region. Consequently, the npn device shows a high detectivity of 1.3 × 1010cmHz1/2W-1at the mid-infrared wavelength, which is significantly improved compared with its single pn junction counterpart. Moreover, it exhibits a fast response speed of 12 μs, and a broadband detection capability ranging from visible to mid-infrared wavelengths.

Ovonic threshold switching-based artificial afferent neurons for thermal in-sensor computing

Artificial afferent neurons in the sensory nervous system inspired by biology have enormous potential for efficiently perceiving and processing environmental information. However, the previously reported artificial afferent neurons suffer from two prominent challenges: considerable power consumption and limited scalability efficiency. Herein, addressing these challenges, a bioinspired artificial thermal afferent neuron based on a N-doped SiTe ovonic threshold switching (OTS) device is presented for the first time. The engineered OTS device shows remarkable uniformity and robust endurance, ensuring the reliability and efficacy of the artificial afferent neurons. A substantially decreased leakage current of the SiTe OTS device by nitrogen doping results in ultra-low power consumption less than 0.3 nJ per spike for artificial afferent neurons. The inherent temperature response exhibited by N-doped SiTe OTS materials allows us to construct a highly compact artificial thermal afferent neuron over a wide temperature range. An edge detection task is performed to further verify its thermal perceptual computing function. Our work provides an insight into OTS-based artificial afferent neurons for electronic skin and sensory neurorobotics.

An artificial visual neuron with multiplexed rate and time-to-first-spike coding

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.

A Spiking Artificial Vision Architecture Based on Fully Emulating the Human Vision

Intelligent vision necessitates the deployment of detectors that are always-on and low-power, mirroring the continuous and uninterrupted responsiveness characteristic of human vision. Nonetheless, contemporary artificial vision systems attain this goal by the continuous processing of massive image frames and executing intricate algorithms, thereby expending substantial computational power and energy. In contrast, biological data processing, based on event-triggered spiking, has higher efficiency and lower energy consumption. Here, this work proposes an artificial vision architecture consisting of spiking photodetectors and artificial synapses, closely mirroring the intricacies of the human visual system. Distinct from previously reported techniques, the photodetector is self-powered and event-triggered, outputting light-modulated spiking signals directly, thereby fulfilling the imperative for always-on with low-power consumption. With the spiking signals processing through the integrated synapse units, recognition of graphics, gestures, and human action has been implemented, illustrating the potent image processing capabilities inherent within this architecture. The results prove the 90% accuracy rate in human action recognition within a mere five epochs utilizing a rudimentary artificial neural network. This novel architecture, grounded in spiking photodetectors, offers a viable alternative to the extant models of always-on low-power artificial vision system.

High-Performance Broadband Image Sensing Photodetector Based on MnTe/WS2 van der Waals Epitaxial Heterostructures

Two-dimensional transition metal dichalcogenide (TMDC) heterostructure is receiving considerable attention due to its novel electronic, optoelectronic, and spintronic devices with design-oriented and functional features. However, direct design and synthesis of high-quality TMDC/MnTe heterostructures remain difficult, which severely impede further investigations of semiconductor/magnetic semiconductor devices. Herein, the synthesis of high-quality vertically stacked WS2/MnTe heterostructures is realized via a two-step chemical vapor deposition method. Raman, photoluminescence, and scanning transmission electron microscopy characterizations reveal the high-quality and atomically sharp interfaces of the WS2/MnTe heterostructure. WS2/MnTe-based van der Waals field effect transistors demonstrate high rectification behavior with rectification ratio up to 106, as well as a typical p-n electrical transport characteristic. Notably, the fabricated WS2/MnTe photodetector exhibits sensitive and broadband photoresponse ranging from UV to NIR with a maximum responsivity of 1.2 × 103 A/W, a high external quantum efficiency of 2.7 × 105%, and fast photoresponse time of ∼50 ms. Moreover, WS2/MnTe heterostructure photodetectors possess a broadband image sensing capability at room temperature, suggesting potential applications in next-generation high-performance and broadband image sensing photodetectors.

Intuition-and-Tactile Bimodal Sensing Based on Artificial-Intelligence-Motivated All-Fabric Bionic Electronic Skin for Intelligent Material Perception

Developing electronic skins (e-skins) with extraordinary perception through bionic strategies has far-reaching significance for the intellectualization of robot skins. Here, an artificial intelligence (AI)-motivated all-fabric bionic (AFB) e-skin is proposed, where the overall structure is inspired by the interlocked bionics of the epidermis-dermis interface inside the skin, while the structural design inspiration of the dielectric layer derives from the branch-needle structure of conifers. More importantly, AFB e-skin achieves intuition sensing in proximity mode and tactile sensing in pressure mode based on the fringing and iontronic effects, respectively, and is simulated and verified through COMSOL finite element analysis. The proposed AFB e-skin in pressure mode exhibits maximum sensitivity of 15.06 kPa-1 (<50 kPa), linear sensitivity of 6.06 kPa-1 (50-200 kPa), and fast response/recovery time of 5.6 ms (40 kPa). By integrating AFB e-skin with AI algorithm, and with the support of material inference mechanisms based on dielectric constant and softness/hardness, an intelligent material perception system capable of recognizing nine materials with indistinguishable surfaces within one proximity-pressure cycle is established, demonstrating abilities that surpass human perception.

Multidimensional detection enabled by twisted black arsenic-phosphorus homojunctions

A light field carrying multidimensional optical information, including but not limited to polarization, intensity and wavelength, is essential for numerous applications such as environmental monitoring, thermal imaging, medical diagnosis and free-space communications. Simultaneous acquisition of this multidimensional information could provide comprehensive insights for understanding complex environments but remains a challenge. Here we demonstrate a multidimensional optical information detection device based on zero-bias double twisted black arsenic-phosphorus homojunctions, where the photoresponse is dominated by the photothermoelectric effect. By using a bipolar and phase-offset polarization photoresponse, the device operated in the mid-infrared range can simultaneously detect both the polarization angle and incident intensity information through direct measurement of the photocurrents in the double twisted black arsenic-phosphorus homojunctions. The device's responsivity makes it possible to retrieve wavelength information, typically perceived as difficult to obtain. Moreover, the device exhibits an electrically tunable polarization photoresponse, enabling precise distinction of polarization angles under low-intensity light exposure. These demonstrations offer a promising approach for simultaneous detection of multidimensional optical information, indicating potential for diverse photonic applications.

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability

Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models. However, hardware neural network reports still mainly focus on shallow networks (2 to 5 layers). Implementing deep neural networks in hardware is challenging due to the layer-by-layer structure, resulting in long training times, signal interference, and low accuracy due to gradient explosion/vanishing. Here, we utilize negative ultraviolet photoconductive light-emitting memristors with intrinsic parallelism and hardware-software co-design to achieve electrical information's optical cross-layer transmission. We propose a hybrid ultra-deep photoelectric neural network and an ultra-deep super-resolution reconstruction neural network using light-emitting memristors and cross-layer block, expanding the networks to 54 and 135 layers, respectively. Further, two networks enable transfer learning, approaching or surpassing software-designed networks in multi-dataset recognition and high-resolution restoration tasks. These proposed strategies show great potential for high-precision multifunctional hardware neural networks and edge artificial intelligence.

Non-volatile rippled-assisted optoelectronic array for all-day motion detection and recognition

In-sensor processing has the potential to reduce the energy consumption and hardware complexity of motion detection and recognition. However, the state-of-the-art all-in-one array integration technologies with simultaneous broadband spectrum image capture (sensory), image memory (storage) and image processing (computation) functions are still insufficient. Here, macroscale (2 × 2 mm2) integration of a rippled-assisted optoelectronic array (18 × 18 pixels) for all-day motion detection and recognition. The rippled-assisted optoelectronic array exhibits remarkable uniformity in the memory window, optically stimulated non-volatile positive and negative photoconductance. Importantly, the array achieves an extensive optical storage dynamic range exceeding 106, and exceptionally high room-temperature mobility up to 406.7 cm2 V-1 s-1, four times higher than the International Roadmap for Device and Systems 2028 target. Additionally, the spectral range of each rippled-assisted optoelectronic processor covers visible to near-infrared (405 nm-940 nm), achieving function of motion detection and recognition.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.