Stereoscopic artificial compound eyes for spatiotemporal perception in three-dimensional space.

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.

Low-Thermal-Budget Ferroelectric Field-Effect Transistors Based on CuInP2S6 and InZnO.

In this paper, we demonstrate low-thermal-budget ferroelectric field-effect transistors (FeFETs) based on the two-dimensional ferroelectric CuInP2S6 (CIPS) and oxide semiconductor InZnO (IZO). The CIPS/IZO FeFETs exhibit nonvolatile memory windows of ∼1 V, low off-state drain currents, and high carrier mobilities. The ferroelectric CIPS layer serves a dual purpose by providing electrostatic doping in IZO and acting as a passivation layer for the IZO channel. We also investigate the CIPS/IZO FeFETs as artificial synaptic devices for neural networks. The CIPS/IZO synapse demonstrates a sizable dynamic ratio (125) and maintains stable multilevel states. Neural networks based on CIPS/IZO FeFETs achieve an accuracy rate of over 80% in recognizing MNIST handwritten digits. These ferroelectric transistors can be vertically stacked on silicon complementary metal-oxide semiconductor (CMOS) with a low thermal budget, offering broad applications in CMOS+X technologies and energy-efficient 3D neural networks.

An artificial neuromuscular junction for enhanced reflexes and oculomotor dynamics based on a ferroelectric CuInP2S6/GaN HEMT.

A neuromuscular junction (NMJ) is a particularized synapse that activates muscle fibers for macro-motions, requiring more energy than computation. Emulating the NMJ is thus challenging owing to the need for both synaptic plasticity and high driving power to trigger motions. Here, we present an artificial NMJ using CuInP2S6 (CIPS) as a gate dielectric integrated with an AlGaN/GaN-based high-electron mobility transistor (HEMT). The ferroelectricity of the CIPS is coupled with the two-dimensional electron gas channel in the HEMT, providing a wide programmable current range of 6 picoampere per millimeter to 5 milliampere per millimeter. The large output current window of the CIPS/GaN ferroelectric HEMT (FeHEMT) allows for amplifier-less actuation, emulating the biological NMJ functions of actuation and synaptic plasticity. We also demonstrate the emulation of biological oculomotor dynamics, including in situ object tracking and enhanced stimulus responses, using the fabricated artificial NMJ. We believe that the CIPS/GaN FeHEMT offers a promising pathway for bioinspired robotics and neuromorphic vision.

In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing.

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.

Effect of Tensile Stress on the Oxide Properties of a Nickel-Based Alloy 600 in Simulated PWR Secondary Water.

The purpose of this work was to examine the effect of tensile stress on the oxide properties of a nickel-based Alloy 600 that was exposed to simulated nuclear steam generator water at 340 °C for 1000 h. The size of the outer oxide particles increased, and the chromium content of the inner oxides decreased under tensile stress. Electrochemical measurements revealed that the charge carrier density increased, and the charge transfer resistance and film resistance were reduced under the tensile stress condition. These changes in the oxide properties are attributed to the formation of short diffusion paths such as line and surface defects due to tensile deformation.