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Arthropods’ eyes are effective biological vision systems for object tracking and wide field of view because of their structural uniqueness; however, unlike mammalian eyes, they can hardly acquire the depth information of a static object because of their monocular cues. Therefore, most arthropods rely on motion parallax to track the object in three-dimensional (3D) space. Uniquely, the praying mantis (Mantodea) uses both compound structured eyes and a form of stereopsis and is capable of achieving object recognition in 3D space. Here, by mimicking the vision system of the praying mantis using stereoscopically coupled artificial compound eyes, we demonstrated spatiotemporal object sensing and tracking in 3D space with a wide field of view. Furthermore, to achieve a fast response with minimal latency, data storage/transportation, and power consumption, we processed the visual information at the edge of the system using a synaptic device and a federated split learning algorithm. The designed and fabricated stereoscopic artificial compound eye provides energy-efficient and accurate spatiotemporal object sensing and optical flow tracking. It exhibits a root mean square error of 0.3 centimeter, consuming only approximately 4 millijoules for sensing and tracking. These results are more than 400 times lower than conventional complementary metal-oxide semiconductor–based imaging systems. Our biomimetic imager shows the potential of integrating nature’s unique design using hardware and software codesigned technology toward capabilities of edge computing and sensing. 

Abstract As machine vision technology generates large amounts of data from sensors, it requires efficient computational systems for visual cognitive processing. Recently, in-sensor computing systems have emerged as a potential solution for reducing unnecessary data transfer and realizing fast and energy-efficient visual cognitive processing. However, they still lack the capability to process stored images directly within the sensor. Here, we demonstrate a heterogeneously integrated 1-photodiode and 1 memristor (1P-1R) crossbar for in-sensor visual cognitive processing, emulating a mammalian image encoding process to extract features from the input images. Unlike other neuromorphic vision processes, the trained weight values are applied as an input voltage to the image-saved crossbar array instead of storing the weight value in the memristors, realizing the in-sensor computing paradigm. We believe the heterogeneously integrated in-sensor computing platform provides an advanced architecture for real-time and data-intensive machine-vision applications via bio-stimulus domain reduction. 

Defect identification has been a significant task in various fields to prevent the potential problems caused by imperfection. There is great attention for developing technology to accurately extract defect information from the image using a computing system without human error. However, image analysis using conventional computing technology based on Von Neumann structure is facing bottlenecks to efficiently process the huge volume of input data at low power and high speed. Herein efficient defect identification is demonstrated via a morphological image process with minimal power consumption using an oxide transistor and a memristor‐based crossbar array that can be applied to neuromorphic computing. Using a hardware and software codesigned neuromorphic system combined with a dynamic Gaussian blur kernel operation, an enhanced defect detection performance is successfully demonstrated with about 104 times more power‐efficient computation compared to the conventional complementary metal‐oxide semiconductor (CMOS)‐based digital implementation. It is believed the back end of line (BEOL)‐compatible all‐oxide‐based memristive crossbar array provides the unique potential toward universal artificial intelligence of things (AIoT) applications where conventional hardware can hardly be used. 

Abstract In‐sensor computing is an emerging architectural paradigm that fuses data acquisition and processing within a sensory domain. The integration of multiple functions into a single domain reduces the system footprint while it minimizes the energy and time for data transfer between sensory and computing units. However, it is challenging for a simple and compact image sensor array to achieve both sensing and computing in each pixel. Here, this work demonstrates a focal plane array with a heterogeneously integrated one‐photodiode one‐resistor (1P‐1R)‐based artificial optical neuron that emulates the sensing, computing, and memorization of a biological retina system. This work employs an InGaAs photodiode featuring a high responsivity and a broad spectrum that covers near‐infrared (NIR) signals and employs an HfO₂memristor as the artificial synapse to achieve the computing/memorization in an analog domain. Using the fabricated focal plane array integrated with an artificial neural network, this work performs in‐sensor image identification of finger veins driven by NIR light illumination (≈84 % accuracy). The proposed in‐sensor image computing architecture that broadly covers the NIR spectrum offers widespread application of focal plane array for computer vision, neuromorphic computing, biomedical engineering, etc. 

Solid-state thermionic devices based on van der Waals structures were proposed for nanoscale thermal to electrical energy conversion and integrated electronic cooling applications. We study thermionic cooling across gold-graphene-WSe 2 -graphene-gold structures computationally and experimentally. Graphene and WSe 2 layers were stacked, followed by deposition of gold contacts. The I - V curve of the structure suggests near-ohmic contact. A hybrid technique that combines thermoreflectance and cooling curve measurements is used to extract the device ZT . The measured Seebeck coefficient, thermal and electrical conductance, and ZT values at room temperatures are in agreement with the theoretical predictions using first-principles calculations combined with real-space Green’s function formalism. This work lays the foundation for development of efficient thermionic devices. 

Strong interactions between excitons are a characteristic feature of two-dimensional (2D) semiconductors, determining important excitonic properties, such as exciton lifetime, coherence, and photon-emission efficiency. Rhenium disulfide (ReS2), a member of the 2D transition-metal dichalcogenide (TMD) family, has recently attracted great attention due to its unique excitons that exhibit excellent polarization selectivity and coherence features. However, an in-depth understanding of exciton-exciton interactions in ReS2 is still lacking. Here we used ultrafast pump-probe spectroscopy to study exciton-exciton interactions in monolayer (1L), bilayer (2L), and triple layer ReS2. We directly measure the rate of exciton-exciton annihilation, a representative Auger-type interaction between excitons. It decreases with increasing layer number, as observed in other 2D TMDs. However, while other TMDs exhibit a sharp weakening of exciton-exciton annihilation between 1L and 2L, such behavior was not observed in ReS2. We attribute this distinct feature in ReS2 to the relatively weak interlayer coupling, which prohibits a substantial change in the electronic structure when the thickness varies. This work not only highlights the unique excitonic properties of ReS2 but also provides novel insight into the thickness dependence of exciton-exciton interactions in 2D systems. 

Abstract Artificial neural networks (ANNs) are widely used in numerous artificial intelligence‐based applications. However, the significant amount of data transferred between computing units and storage has limited the widespread deployment of ANN for the artificial intelligence of things (AIoT) and power‐constrained device applications. Therefore, among various ANN algorithms, quantized neural networks (QNNs) have garnered considerable attention because they require fewer computational resources with minimal energy consumption. Herein, an oxide‐based ternary charge‐trap transistor (CTT) that provides three discrete states and non‐volatile memory characteristics are introduced, which are desirable for QNN computing. By employing a differential pair of ternary CTTs, an artificial synaptic segregation with multilevel quantized values for QNNs is demostrated. The approach establishes a platform that combines the advantages of multiple states and robustness to noise for in‐memory computing to achieve reliable QNN performance in hardware, thereby facilitating the development of energy‐efficient AIoT.