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Sun et al. Soft Sci. 2025, 5, 18 https://dx.doi.org/10.20517/ss.2024.77 Page 17 of 26

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edge computing [Figure 6H].

The integration of memristors, transistors, and flexible sensors in neuromorphic computing systems has the
potential to revolutionize both AI and biological signal processing. By combining the unique properties of
memristors - such as their ability to “remember” electrical states - with advanced algorithms such as ANN
and SNN, researchers are creating more efficient, adaptive, and intelligent systems. The development of
system-on-chip (SOC) solutions will further enhance the practicality and energy efficiency of neuromorphic
computing, enabling a wide range of applications in fields such as robotics, healthcare, Internet of Things
(IoT), and environmental monitoring.

ON-CHIP NEUROMORPHIC COMPUTING SYSTEM
Traditional analog computing systems suffer from limitations due to environmental noise and high energy
consumption. By integrating neuromorphic computing SOC, these challenges can be mitigated. On-chip
learning and tightly coupled analog computing frameworks enable the design of more compact, energy-
efficient systems. Neuromorphic SOC can perform real-time processing tasks such as pattern recognition,
sensory input analysis, and adaptive decision-making. The on-chip integration of learning algorithms and
neuromorphic hardware is a promising direction for future developments in skin-inspired neuromorphic
sensors. This emerging technology has made significant strides in enhancing adaptive sensing and
processing capabilities in intelligent systems. By mimicking the sensory mechanisms of human skin and the
information-processing capabilities of the nervous system, it aims to enable more sophisticated real-time
interactions and decision-making in complex environments. By combining skin-inspired sensors with
neuromorphic computing, instant multimodal sensing (e.g., touch, temperature, pressure) can be achieved.
Leveraging the principles of the biological nervous system, these technologies enable the processing of
complex sensory data and its wireless transmission. This capability allows for the provision of highly
sensitive, real-time feedback, thereby enhancing the system’s ability to rapidly adapt and respond to
dynamic environmental changes [104,105] [Figure 7A and B]. Integrated systems that combine skin-inspired
sensors and neuromorphic computing can generally be categorized into the following areas.

E-skin, designed to mimic the sensory functions of human skin, enables the detection of various stimuli
such as pressure, temperature, humidity, and touch. By integrating piezoelectric and thermoelectric
materials, it can sense multiple tactile signals. Furthermore, by combining neuromorphic computing, e-skin
can simulate human skin’s perception and response mechanisms, allowing for adaptive sensing and
intelligent responses. Researchers have reported a multifunctional e-skin that combines multiple sensory
functions with intelligent robot control. Through this skin, robots could interact with humans safely and
accurately [Figure 7C]. Other researchers have observed self-repair in conductive nanostructures and
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dynamic cross-linked polymer networks, enabling the integration of interconnected sensors and lighting
devices into a single multifunctional system. It was the first self-repairing, stretchable multi-component e-
skin, offering new directions for e-skin development [Figure 7D]. Additionally, researchers have
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developed multifunctional e-skin via in-situ 3D printing, capable of hair growth with high precision and
consistency. It included temperature, pressure, and tactile sensor arrays that accurately recognize various
stimuli at different positions [Figure 7E].
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The combination of skin-inspired sensors with neuromorphic computing enables prosthetics to respond
more naturally and precisely. Researchers have developed a hand posture recognition system using surface
electromyographic signals from the flexor and extensor muscles, allowing precise control of bionic hands. A
dual-channel surface electromyography (EMG) signal recognition system could identify hand postures and
control the corresponding gestures of a custom-built bionic hand . Combining skin-inspired sensors with
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