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Soft bioelectronic devices provide new opportunities for next-generation implantable devices owing to their soft mechanical nature that leads to minimal tissue damages and immune responses. However, a soft form of the implantable optoelectronic device for optical sensing and retinal stimulation has not been developed yet because of the bulkiness and rigidity of conventional imaging modules and their composing materials. Here, we describe a high-density and hemispherically curved image sensor arr

Conventional imaging and recognition systems require an extensive amount of data storage, pre-processing, and chip-to-chip communications as well as aberration-proof light focusing with multiple lenses for recognizing an object from massive optical inputs. This is because separate chips (i.e., flat image sensor array, memory device, and CPU) in conjunction with complicated optics should capture, store, and process massive image information independently. In contrast, human vision employs a highl

Soft bioelectronics intended for application to wearable and implantable biomedical devices have attracted great attention from material scientists, device engineers, and clinicians because of their extremely soft mechanical properties that match with a variety of human organs and tissues, including the brain, heart, skin, eye, muscles, and neurons, as well as their wide diversity in device designs and biomedical functions that can be finely tuned for each specific case of applications. These un

Abstract Soft electronic devices, particularly for healthcare applications, have been intensively studied over the past decade owing to their unique advantages over conventional rigid electronics. These advantages include conformal contacts on target tissues such as the skin, heart, and brain along with a high deformability that minimizes unwanted inflammatory responses. To achieve mechanically soft but multifunctional high performance electronics for wearable and implantable biomedical devices,

Synaptic photodetectors exhibit photon-triggered synaptic plasticity, which thus can improve the image recognition rate by enhancing the image contrast. However, still, the visualization and recognition of invisible ultraviolet (UV) patterns are challenging, owing to intense background noise. Here, inspired by all-or-none potentiation of synapse, we develop an integrated device of synaptic phototransistors (SPTrs) and quantum dot light-emitting diodes (QLEDs), facilitating noise reduction and vi

High-performance robotic vision empowers mobile and humanoid robots to detect and identify their surrounding objects efficiently, which enables them to cooperate with humans and assist human activities. For error-free execution of these robots' tasks, efficient imaging and data processing capabilities are essential, even under diverse and complex environments. However, conventional technologies fall short of meeting the high-standard requirements of robotic vision under such circumstances. Here,

With the advance in information technologies involving machine vision applications, the demand for energy- and time-efficient acquisition, transfer, and processing of a large amount of image data has rapidly increased. However, current architectures of the machine vision system have inherent limitations in terms of power consumption and data latency owing to the physical isolation of image sensors and processors. Meanwhile, synaptic optoelectronic devices that exhibit photoresponse similar to th

High-performance chiroptical synaptic phototransistors are successfully demonstrated using heterojunctions composed of a self-assembled nanohelix of a π-conjugated molecule and a metal oxide semiconductor. To impart strong chiroptical activity to the device, a diketopyrrolopyrrole-based π-conjugated molecule decorated with chiral glutamic acid is newly synthesized; this molecule is capable of supramolecular self-assembly through noncovalent intermolecular interactions. In particular, nanohelix f

Conventional imaging and data processing devices are not ideal for mobile artificial vision applications, such as vision systems for drones and robots, because of the heavy and bulky multilens optics in the camera modules. Furthermore, the physically isolated image data processing units of conventional systems induce large power consumption and data latency. For mobile artificial vision applications, electronic eyes, including neuromorphic ones, have been developed inspired by biological eyes an

The bioinspired camera, comprising a single lens and a curved image sensor-a photodiode array on a curved surface-, was born of flexible electronics. Its economical build lends itself well to space-constrained machine vision applications. The curved sensor, much akin to the retina, helps image focusing, but the curvature also creates a problem of image distortion, which can undermine machine vision tasks such as object recognition. Here we report an anti-distortion single-lens camera, where 4096

Robotic vision has traditionally relied on high-performance yet resource-intensive computing solutions, which necessitate high-throughput data transmission from vision sensors to remote computing servers, sacrificing energy efficiency and processing speed. A promising solution is data compaction through contour extraction, visualizing only the outlines of objects while eliminating superfluous backgrounds. Here, we introduce an in-sensor multilevel image adjustment method using adjustable synapti

CuIn(Ga)Se2 (CIGS) thin films were fabricated by electrochemical deposition method in a single bath in which ionized Cu, In, Ga, and Se were contained. The electrolyte was prepared by dissolving CuCl2, InCl3, GaCl3, H2SeO3, and LiCl (as pH adjuster) in deionized water. To modify the surface and microstructures of CIGS films, various amounts of gelatin were added in the solution. Potentiostatic deposition process was achieved by applying a voltage ranging from −640 to −1100 mV vs. Ag/AgCl. We pre