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. Significance We show a near–hysteresis-free piezoresistive sensor system from soft materials by using a method of generating unique three-dimensional nanoscale crack morphologies on metal-coated elastomeric microstructures. Our method addresses the major challenge of inherent electromechanical hysteresis faced by many electronic sensor skins using soft materials when large compressive pressures are applied. Piezoresistive sensors made from our flexible composite material track deformation with <3% hysteresis and can be reliably used as a healthcare wearable device for measuring pulse wave velocity on a small skin patch. We also showed that the low hysteresis enabled accurate and reliable single-touch classification of surface textures on textile surfaces for robotic applications.

Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors that detect physiological and environmental signals have been designed and integrated into functional systems. Recently, researchers have increasingly deployed machine learning and other artificial intelligence (AI) technologies to mimic the human neural system for the processing and analysis of sensory data collected by e-skins. Integrating AI has the potential to enable advanced applications in robotics, healthcare, and human-machine interfaces but also presents challenges such as data diversity and AI model robustness. In this review, we first summarize the functions and features of e-skins, followed by feature extraction of sensory data and different AI models. Next, we discuss the utilization of AI in the design of e-skin sensors and address the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks. Subsequently, we explore hardware-layer in-skin intelligence before concluding with an analysis of the challenges and opportunities in the various aspects of AI-enabled e-skins.

Flexible pressure sensors are crucial for E-skins to enable tactile sensing capabilities. However, flexible pressure sensors often exhibit high hysteretic response caused by internal and external mechanical dissipations in flexible materials. The hysteresis gives rise to reliability issues, especially in the presence of dynamic stress. In this letter, we report a flexible capacitive pressure sensor design with hierarchically microstructured electrodes to obtain both high sensitivity and low hysteresis. The sparsely spaced large pyramid microstructure improves the sensitivity, whereas the small pyramid reduces the hysteresis caused by interfacial adhesion. The optimized sensor shows excellent performances in terms of high sensitivity (~3.73 kPa⁻¹), ultralow detection limits (0.1 Pa), significantly reduced hysteresis (~4.42%), and enhanced sensing capability for pluses, which demonstrates its potential for advanced electronic skins.

Flexible pressure sensors are the cornerstone of electronic skins with tactile function. However, flexible pressure sensors tend to have disadvantages of slow response speed and large hysteresis, which are caused mainly by the viscoelasticity and interfacial adhesion of flexible materials. In this letter, we adopted silicon nanowire (SiNW) arrays to fabricate a pressure sensor with a piezo-capacitance when the SiNWs are bending under pressure, which avoided the viscoelasticity and interfacial adhesion accompanying with common flexible materials. The sensor recorded unprecedented low hysteresis (~2.26%) and an extremely short response time (~3 ms). With high sensitivity (up to ~8.2 kPa ^-1 ), large response capacitance, ultralow detection limit (0.1 Pa), and good repeatability and stability, the sensor holds potential applications in advanced electronic skins, especially when high-precision and high-speed sensing are required.

The rise of the Internet of things has catalyzed extensive research in the realm of flexible wearable sensors. In comparison with conventional sensor power supply methods that are reliant on external sources, self-powered sensors offer notable advantages in wearable comfort, device structure, and functional expansion. The energy-harvesting modes dominated by piezoelectric nanogenerators (PENGs), triboelectric nanogenerators (TENGs), and pyroelectric nanogenerators (PyENGs) create more possibilities for flexible self-powered sensors. This paper meticulously examines the progress in flexible self-powered devices harnessing TENG, PENG, and PyENG technologies and highlights the evolution of these sensors concerning the material selection, pioneering manufacturing techniques, and device architecture. It also focuses on the research progress of sensors with composite power generation modes. By amalgamating pivotal discoveries and emerging trends, this review not only furnishes a comprehensive portrayal of the present landscape but also accentuates avenues for future research and the application of flexible self-powered sensor technology.