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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.

Existing transfer technologies in the construction of film-based electronics and devices are deeply established in the framework of native solid substrates. Here, we report a capillary approach that enables a fast, robust, and reliable transfer of soft films from liquid in a defect-free manner. This capillary transfer is underpinned by the transfer front of dynamic contact among receiver substrate, liquid, and film, and can be well controlled by a selectable motion direction of receiver substrates at a high speed. We demonstrate in extensive experiments, together with theoretical models and computational analysis, the robust capabilities of the capillary transfer using a versatile set of soft films with a broad material diversity of both film and liquid, surface-wetting properties, and complex geometric patterns of soft films onto various solid substrates in a deterministic manner.

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The time-of-flight (ToF) principle is a method used to measure distance and construct three-dimensional (3D) images by detecting the time or the phase difference between emitted and back-reflected optical flux. The ToF principle has been employed for various applications including light ranging and detection (LiDAR), machine vision and biomedical engineering; however, bulky system size and slow switching speed have hindered the widespread application of ToF technology. To alleviate these issues, a demonstration of hetero-integration of GaN-based high electron mobility transistors (HEMTs) and GaAs-based vertical cavity surface emitting lasers (VCSELs) on a single platform via a cold-welding method was performed. The hetero-integrated ToF sensors show superior switching performance when compared to silicon-transistor-based systems, miniaturizing size and exhibiting stable ranging and high-resolution depth-imaging. This hetero-integrated system of dissimilar material-based highperformance devices suggests a new pathway towards enabling high-resolution 3D imaging and inspires broader range application of heterogeneously integrated electronics and optoelectronics.

Automated defect inspection technology has been widely investigated in a variety of fields, including the semiconductor and integrated circuit industry, [1-3] engineering and science, [4,5] and medical applications, [6,7] to overcome the drawbacks of manual detection methods, which are imprecise and timeconsuming. Various image processing approaches of defect identification such as filtering, structuring, and statistical methods have been recently developed to recognize the defective information from input images. [8-10] However, most defects are of irregular shape and size, while the inspection area is large, and thus the complexity of the input data for defect inspection is usually high. Therefore, high computational power along with large power consumption and long processing times is generally required to operate defect identification algorithms using the von Neumann architecture based computing system due to a memory wall problem between the microprocessor and storage memory. [11] To ameliorate these issues, here we have used a nonvolatile memory based neuromorphic computing system that imitates the human brain's operation combined with a software algorithm for the defect identification process. [12,13] Among them, memristor-based crossbar array based neuromorphic computing has received great attention due to its scalability and computing-in-memory features. [13-18] Cross-point structured memristors, nevertheless, have suffered from cell-to-cell interferences due to the sneak path currents through neighboring memristor pixels that lead to unnecessary power dissipation and inaccurate operations. [19] To precisely modulate the resistance of each memristor, the memristor-based array is usually integrated with a selector device (switching component) such as silicon (Si)-metal-oxide field-effect transistor (MOSFET), which is known as 1-transistor and 1-resistor (1T1R) structure. [20-23] The 1T1R architecture allows us to precisely program individual memristors using the