The human sense of touch is essential for dexterous tool usage, spatial awareness, and social communication. Equipping intelligent human-like androids and prosthetics with electronic skins—a large array of sensors spatially distributed and capable of rapid somatosensory perception—will enable them to work collaboratively and naturally with humans to manipulate objects in unstructured living environments. Previously reported tactile-sensitive electronic skins largely transmit the tactile information from sensors serially, resulting in readout latency bottlenecks and complex wiring as the number of sensors increases. Here, we introduce the Asynchronously Coded Electronic Skin (ACES)—a neuromimetic architecture that enables simultaneous transmission of thermotactile information while maintaining exceptionally low readout latencies, even with array sizes beyond 10,000 sensors. We demonstrate prototype arrays of up to 240 artificial mechanoreceptors that transmitted events asynchronously at a constant latency of 1 ms while maintaining an ultra-high temporal precision of <60 ns, thus resolving fine spatiotemporal features necessary for rapid tactile perception. Our platform requires only a single electrical conductor for signal propagation, realizing sensor arrays that are dynamically reconfigurable and robust to damage. We anticipate that the ACES platform can be integrated with a wide range of skin-like sensors for artificial intelligence (AI)–enhanced autonomous robots, neuroprosthetics, and neuromorphic computing hardware for dexterous object manipulation and somatosensory perception.

Human skin is a self-healing mechanosensory system that detects various mechanical contact forces efficiently through three-dimensional innervations. Here, we propose a biomimetic artificially innervated foam by embedding three-dimensional electrodes within a new low-modulus self-healing foam material. The foam material is synthesized from a one-step self-foaming process. By tuning the concentration of conductive metal particles in the foam at near-percolation, we demonstrate that it can operate as a piezo-impedance sensor in both piezoresistive and piezocapacitive sensing modes without the need for an encapsulation layer. The sensor is sensitive to an object’s contact force directions as well as to human proximity. Moreover, the foam material self-heals autonomously with immediate function restoration despite mechanical damage. It further recovers from mechanical bifurcations with gentle heating (70 °C). We anticipate that this material will be useful as damage robust human-machine interfaces.

Electronic skins are essential for real-time health monitoring and tactile perception in robots. Although the use of soft elastomers and microstructures have improved the sensitivity and pressure-sensing range of tactile sensors, the intrinsic viscoelasticity of soft polymeric materials remains a long-standing challenge resulting in cyclic hysteresis. This causes sensor data variations between contact events that negatively impact the accuracy and reliability. Here, we introduce the Tactile Resistive Annularly Cracked E-Skin (TRACE) sensor to address the inherent trade-off between sensitivity and hysteresis in tactile sensors when using soft materials. We discovered that piezoresistive sensors made using an array of three-dimensional (3D) metallic annular cracks on polymeric microstructures possess high sensitivities (> 107 Ω ⋅ kPa−1), low hysteresis (2.99 ± 1.37%) over a wide pressure range (0–20 kPa), and fast response (400 Hz). We demonstrate that TRACE sensors can accurately detect and measure the pulse wave velocity (PWV) when skin mounted. Moreover, we show that these tactile sensors when arrayed enabled fast reliable one-touch surface texture classification with neuromorphic encoding and deep learning algorithms.

Stretchable electronics have advanced rapidly and many applications require high repeatability and robustness under various mechanical deformations. It has been described here that how a highly stretchable and reliable conductor composite made from helical copper wires and a soft elastomer, named eHelix, can provide mechanically robust and strain‐insensitive electronic conductivity for wearable devices. The reversibility of the mechanical behavior of the metal‐elastomer system has been studied using finite element modeling methods. Optimal design parameters of such helical metal‐elastomer structures are found. The scaling of multiple copper wires into such helical shapes to form a Multi‐eHelix system is further shown. With the same elastomer volume, Multi‐eHelix has more conductive paths and a higher current density than the single‐eHelix. Integrations of these eHelix stretchable conductors with fabrics showed wearable displays that can survive machine‐washes and hundreds of mechanical loading cycles. The integration of the eHelix developed by us with a wearable optical heart rate sensor enabled a wearable health monitoring system that can display measured heart rates on clothing. Furthermore, Multi‐eHelix conductors are used to connect flexible printed circuit boards and piezoresistive sensors on a tactile sensing glove for the emerging sensorized prosthetics.

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