Quantum imaging of the reconfigurable VO2 synaptic electronics for neuromorphic computing.

Neuromorphic computing has shown remarkable capabilities in silicon-based artificial intelligence, which can be optimized by using Mott materials for functional synaptic connections. However, the research efforts focus on two-terminal artificial synapses and envisioned the networks controlled by silicon-based circuits, which is difficult to develop and integrate. Here, we propose a dynamic network with laser-controlled conducting filaments based on electric field-induced local insulator-metal transition of vanadium dioxide. Quantum sensing is used to realize conductivity-sensitive imaging of conducting filament. We find that the location of filament formation is manipulated by focused laser, which is applicable to simulate the dynamical synaptic connections between the neurons. The ability to process signals with both long-term and short-term potentiation is further demonstrated with ~60 times on/off ratio while switching the pathways. This study opens the door to the development of dynamic network structures depending on easily controlled conduction pathways, mimicking the biological nervous systems.

Deep-Learning Enabled Active Biomimetic Multifunctional Hydrogel Electronic Skin.

There is huge demand for recreating human skin with the functions of epidermis and dermis for interactions with the physical world. Herein, a biomimetic, ultrasensitive, and multifunctional hydrogel-based electronic skin (BHES) was proposed. Its epidermis function was mimicked using poly(ethylene terephthalate) with nanoscale wrinkles, enabling accurate identification of materials through the capabilities to gain/lose electrons during contact electrification. Internal mechanoreceptor was mimicked by interdigital silver electrodes with stick-slip sensing capabilities to identify textures/roughness. The dermis function was mimicked by patterned microcone hydrogel, achieving pressure sensors with high sensitivity (17.32 mV/Pa), large pressure range (20-5000 Pa), low detection limit, and fast response (10 ms)/recovery time (17 ms). Assisted by deep learning, this BHES achieved high accuracy and minimized interference in identifying materials (95.00% for 10 materials) and textures (97.20% for four roughness cases). By integrating signal acquisition/processing circuits, a wearable drone control system was demonstrated with three-degree-of-freedom movement and enormous potentials for soft robots, self-powered human-machine interaction interfaces of digital twins.

A robust fiber-based quantum thermometer coupled with nitrogen-vacancy centers.

The nitrogen-vacancy center in diamond has been broadly applied in quantum sensing since it is sensitive to different physical quantities. Meanwhile, it is difficult to isolate disturbances from unwanted physical quantities in practical applications. Here, we present a fiber-based quantum thermometer by tracking the sharp-dip in the zero-field optically detected magnetic resonance spectrum in a high-density nitrogen-vacancy ensemble. Such a scheme can not only significantly isolate the magnetic field and microwave power drift but also improve the temperature sensitivity. Thanks to its simplicity and compatibility in implementation and robustness, this quantum thermometer is then applied to the surface temperature imaging of an electronic chip with a sensitivity of 18mK/Hz. It thus paves the way to high sensitive temperature measurements in ambiguous environments.