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Page 2 of 26 Sun et al. Soft Sci. 2025, 5, 18 https://dx.doi.org/10.20517/ss.2024.77

remarkable sensitivity and precision. This sensory capability enables dynamic interaction with
environments, performing complex tasks and maintaining homeostasis. Replicating these diverse sensing
functions in artificial systems is a key research area in materials science, robotics, and wearable
[1,2]
technology . Skin-inspired sensors, also known as artificial skin, are designed to mimic the mechanical,
electrical, and sensory properties of human skin. In addition to mimicking basic sensory functions,
multifunctional sensors integrate multiple sensing modalities into a single platform that can simultaneously
detect various environmental factors such as strain, humidity, and biochemical markers . These sensors
[3,4]
typically utilize flexible or stretchable materials that can detect a range of environmental stimuli, from
mechanical pressure and tactile feedback to changes in temperature and humidity. Advanced materials,
such as dielectric elastomers, conductive polymers, and piezoelectric materials, provide the necessary
[5]
sensitivity and responsiveness for real-time sensory feedback .
However, the perception information signals from these flexible sensors are continuous analog signals,
which can be converted into discrete digital signals using traditional analog-to-digital converters (ADC) .
[6,7]
This conversion process results in massive data processing requirements, leading to increased need for
communication speed and energy costs due to the distance between memory and computing units. In
contrast, biological sensory organs can in situ detect and process external stimuli, transmitting the
processed information directly to the brain for final analysis and decision-making. The human brain is a
complex system consisting of a network of approximately 100 billion neurons interconnected through 100
trillion synapses. The power consumption of the brain performing its incredible feats is nearly 20 W,
[8,9]
whereas a standard computer needs about 250 W to accomplish the same tasks .
To overcome these challenges, researchers have investigated neuromorphic computing systems, which are
inspired by the neuro-synaptic framework of the human brain. When combined with neuromorphic
computing technology, skin-inspired neuromorphic sensors have shown great potential to create truly
intelligent, adaptive systems capable of responding to sensory input in a manner that mirrors human-like
cognition and decision-making capabilities . By processing sensory data locally and efficiently, these
[10]
systems can reduce latency, lower energy consumption, and enable more responsive interactions with the
environment. Neuromorphic systems leverage massive parallelism to reduce energy consumption in signal
[11]
processing systems and address the bottlenecks of the von Neumann architecture . Unlike traditional
architectures, neuromorphic computing integrates memory and processing within the same module,
facilitating compute-in-memory (CIM) capabilities that enhance communication speed and reduce energy
costs. Traditional complementary metal-oxide semiconductor (CMOS) technology relies on intricate
auxiliary circuits and large capacitors to emulate biodynamics, posing challenges in large-area
manufacturing, device integration, and complex circuit design.

To address these issues, researchers have focused on developing artificial synapses by leveraging emerging
nonvolatile memory devices, such as nonvolatile memristors, diffusive memristors, synaptic transistors, etc.
Although volatile memory devices are not typically used for long-term data storage, complementary
nonvolatile devices can provide fast, short-term memory capabilities for dynamic processing. Synaptic
transistors can be designed in various configurations, such as bottom-gated, side-gated, floating gate, or top-
gated, depending on the desired electrical characteristics and applications . These different gate structures
[12]
offer flexibility in controlling the characteristics of transistors, such as threshold voltage, switching speed,
and retention properties, showing potential for mimicking synaptic functions in neuromorphic systems .
[13]
Since the first oxide-based resistive switches were demonstrated as memristors in 2008, memristors have
been used as computing units in the form of crossbar arrays. A crossbar array could perform parallel matrix
calculations, enabling CIM capabilities [14,15] . Memristors play an important role in motion detection , robot
[16]