Mincheol Lee

High-density stretchable pixelated electronics that conform to complex, non-developable surfaces are key enablers of next-generation robotic vision systems, skin-mountable sensors and wearable electronics. Existing strategies for stretchable pixelated device arrays—such as buckling mechanics, pop-up structures, serpentine interconnects and kirigami designs—have offered partial solutions. However, a strategy that simultaneously achieves high device performance, stretchability, fill factor and integration density has remained elusive. Here we present a next-generation platform for stretchable pixelated electronics that integrates high-performance single-crystalline silicon pixels with finely patterned liquid metal interconnects on ultrathin elastomeric substrates. We developed a monolithic microfabrication strategy that enables customizable, densely packed and mechanically compliant pixelated device arrays. Using this platform, we demonstrate shape-reconfigurable imaging devices that achieve superb fill factors of up to 81% and a biaxial stretchability of 100%. We also present a human-eye-inspired robotic vision system with tunable focus and a skin-mountable lens-free imaging system. The complementary operation of retinal and epidermal imaging systems enables multiscale visual acquisition and depth sensing.

Shape-memory materials are widely utilized in biomedical devices and tissue engineering, particularly for their ability to undergo predefined shape changes in response to external stimuli. In this study, a shape-transformable organohydrogel was developed by incorporating a gallium mesh into a polyacrylamide/alginate/glycerol matrix. The gallium mesh, which transitions between solid and liquid states at moderate temperatures (~29.8 °C), enhanced the hydrogel’s mechanical properties and enabled shape-memory functionality. The composite organohydrogel exhibited a high elastic modulus of ~900 kPa in the solid gallium state and ~30 kPa in the liquid gallium state, enabling reversible deformation and structural stability. Glycerol improved the hydrogel’s moisture retention, maintaining stretchability and repeated heating and cooling cycles. After multiple cycles of the shape-changing process, the organohydrogel retained its mechanical integrity, achieving shape-fixation and recovery ratios of ~96% and 95%, respectively. This combination of shape-memory functionality, stretchability, and mechanical stability makes this organohydrogel highly suitable for applications in flexible electronics, soft robotics, and biomedical devices, where adaptability and shape retention are essential.

Biologically inspired artificial vision research has led to innovative robotic vision systems with low optical aberration, wide field of view, and compact form factor. However, challenges persist in object detection and recognition against complex backgrounds and varied lighting. Inspired by the feline eye, which features a vertically elongated pupil and tapetum lucidum, this study introduces an artificial vision system designed for superior object detection and recognition in a monocular framework. Using a slit-like elliptical aperture and a patterned metal reflector beneath a hemispherical silicon photodiode array, the system reduces excessive light and enhances photosensitivity. This design achieves clear focus under bright light and enhanced sensitivity in dim conditions. Theoretical and experimental analyses demonstrate the system’s ability to filter redundant information and detect camouflaged objects in diverse lighting, representing a substantial advancement in monocular camera technology and the potential of biomimicry in optical innovations.

Avian eyes have deep central foveae as a result of extensive evolution. Deep foveae efficiently refract incident light, creating a magnified image of the target object and making it easier to track object motion. These features are essential for detecting and tracking remote objects in dynamic environments. Furthermore, avian eyes respond to a wide spectrum of light, including visible and ultraviolet light, allowing them to efficiently distinguish the target object from complex backgrounds. Despite notable advances in artificial vision systems that mimic animal vision, the exceptional object detection and targeting capabilities of avian eyes via foveated and multispectral imaging remain underexplored. Here, we present an artificial vision system that capitalizes on these aspects of avian vision. We introduce an artificial fovea and vertically stacked perovskite photodetector arrays whose designs were optimized by theoretical simulations for the demonstration of foveated and multispectral imaging. The artificial vision system successfully identifies colored and mixed-color objects and detects remote objects through foveated imaging. The potential for use in uncrewed aerial vehicles that need to detect, track, and recognize distant targets in dynamic environments is also discussed. Our avian eye–inspired perovskite artificial vision system marks a notable advance in bioinspired artificial visions.