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Sun et al. Soft Sci. 2025, 5, 18 https://dx.doi.org/10.20517/ss.2024.77 Page 3 of 26

[17]
navigation , and so on. In addition to oxide-based resistive switches, other resistive neuromorphic
compute units, including ferroelectric artificial synapses [18,19] , ion-intercalation resistors, and memtransistors
[20]
with a three-terminal structure, exhibited similar properties . Recently, memristors based on atomically
thin sheets of organic and inorganic materials, such as 2D hexagonal boron nitride and organic polymers
such as polyimide covalent organic framework (PI-NT COF), 2,7-dioctyl[1]benzothieno[3,2-
b][1]benzothiophene (C -BTBT), poly(vinylidenefluoride-co-trifluoroethylene) (PVDF-TrFE), metal-
8
organic frameworks (MOFs), etc., have been fabricated. These memristors exhibit fast switching ratios and
low energy consumption [21-25] , resulting in high computation efficiency to support human-like data
processing in neuromorphic systems. The fabrication of these devices can be achieved through techniques
such as photolithography, printing methods, dry etching and other advanced fabrication processes, enabling
scalable and cost-effective production for integration into artificial intelligence (AI) and electronic
circuits [26-28] .

Intelligent algorithms enhance skin-inspired neuromorphic sensor systems by boosting their adaptability,
data processing efficiency, decision-making accuracy, and energy efficiency, enabling them to respond
dynamically and autonomously in complex environments. They will lead to more precise, real-time
performance and proactive capabilities across a range of smart applications. With advancements in AI,
particularly through artificial neural networks (ANNs) and spiking neural networks (SNNs), sensor systems
can process data more intelligently, make better decisions, and interact more effectively with their
surroundings. Early ANNs, known as perceptrons, were restricted to solving linear classification
[29]
problems . With the development of ANNs, the backpropagation algorithm was introduced to train neural
networks with hidden layers. However, the limitations of traditional ANNs, such as high computational
costs, energy inefficiency, and inability to handle temporal data effectively, have driven the need for more
advanced models. After that, the emergence of the third generation of ANNs, known as SNNs , presented
[26]
an event-driven signal processing approach and a promising alternative for breaking the von Neumann
bottleneck. Compared with ANNs, the biggest challenge for SNNs is the training approach. Due to their
complex dynamics and the non-differentiable nature of spiking pulses, backpropagation algorithms cannot
be directly used in SNNs. The connection strength among neurons depends on the relative time difference
between the pulses emitted by presynaptic and postsynaptic neurons. Based on this principle, spike-timing-
[27]
dependent plasticity (STDP) is used to tune the connection strength in synapses . However, the accuracy
of SNN algorithms is weaker than that of ANNs. To improve the accuracy of SNNs, some new technologies
[30]
inspired by ANNs are being employed to address the training challenges .

Building upon recent advancements in neuromorphic computing and artificial synapses, researchers have
explored the synergy between intelligent processing systems and sophisticated sensory inputs [31-33] . Human
skin covers a vast surface area and integrates diverse sensory modalities, necessitating sensor arrays that can
similarly encompass extensive regions to accurately replicate its sensory capabilities. To effectively replicate
the comprehensive sensory capabilities, the integration of skin-inspired sensors with neuromorphic
architecture holds significant potential for enhancing the adaptability and efficiency of artificial sensory
platforms. By merging multimodal sensing artificial skin with parallel processing neuromorphic devices, it
becomes feasible to develop neuromorphic sensors that closely emulate the responsiveness and decision-
making processes of the human body [34-36] . Despite these advantages, there are still significant challenges to
be addressed in the development of neuromorphic sensors. Traditional CMOS technology, which is widely
used in the fabrication of electronic devices, faces limitations in terms of large-area manufacturing, device
integration, and complex circuit design when attempting to mimic the biodynamics of biological systems.
Additionally, the integration of emerging memory devices, such as memristors and synaptic transistors, into
neuromorphic systems remains a challenging yet essential area of research. These devices offer the potential