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Page 10 of 26 Sun et al. Soft Sci. 2025, 5, 18 https://dx.doi.org/10.20517/ss.2024.77

various scenarios is a complex challenge. Over-optimization of one feature could adversely affect the
measurement accuracy of other fields. Furthermore, as sensors are used over time, their performance may
degrade, and there is a lack of long-term performance testing in current research. Inspired by the excellent
decoupling capabilities of human skin, a 3D architecture for an e-skin (3DEA) device was developed to
simultaneously measure the modulus and curvature of an object through normal force, shear force, and
strain. However, it integrated only a 5 × 5 array of multifunctional sensor units (each sensor is
approximately 12 mm × 12 mm) [Figure 3G]. These sensor arrays often suffer from crosstalk, reduced
[73]
performance, and challenges in functional integration. To address the issue of area, researchers have
designed an ion-electron pressure sensor integrated with a 32 × 32 array of pressure sensor units. These
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skin-inspired iontronic sensors exhibit unexpectedly high sensitivity (365 kPa ) and an ultra-broad range
(1.7 Pa to 1,000 kPa), with a total size of approximately 32 cm × 32 cm. Although this sensor array featured a
large scale, it only served a single function .
[74]
Current research faces limitations in sensor array size, sensing functions, and performance, which fail to
meet the integration requirements of advanced systems. Additionally, most sensor arrays lack intelligence,
with little integration of neuromorphic devices such as memristors or neural network algorithms to enhance
performance. Combining these elements can significantly improve sensor capabilities. Future research
should focus on developing large-scale, multifunctional, and intelligent sensor arrays to address these
challenges.

NEURAL DEVICES
In biology, signals are conveyed through neurons interconnected in complex networks. When a signal
reaches a certain threshold, it is transmitted to the next neuron as a positive signal. Currently, memristors
and transistors serve as synaptic devices in neuromorphic computing. A transistor consists of a channel
layer made of semiconductor materials, a dielectric layer, and source and drain terminals. By tuning the gate
voltage, the current from source to drain can update synaptic weights. Neuromorphic computing using
memristors focuses on mimicking the brain’s synaptic functions. A memristor is a novel device that
represents the relationship between magnetic flux and charge, alongside resistors, capacitors, and inductors.
Memristors typically comprise electrode layers and functional material layers. Functional materials in
memristors are classified into inorganic and organic categories. Inorganic materials include HfOx, NiOx,
TiOx, TaOx, etc., while organic materials encompass conductive polymers, such as poly(3,4-
ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), Nafion and other small organic
molecules [75-77] . Hewlett-Packard (HP) was the first to experimentally verify the existence of memristors in
2008 by introducing a device composed of a double layer of TiO thin film (Pt/TiO /Pt) . Since this
[78]
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groundbreaking discovery, memristors have been extensively utilized to model synaptic connections in
simulated neural networks. By integrating memristors with components such as capacitors, researchers can
effectively replicate the signal transmission mechanisms of biological synapses, thereby enhancing the
complexity and efficiency of neuromorphic systems. Advanced artificial neurons utilizing volatile NbOx
memristors have been developed to execute threshold-driven spiking and spatiotemporal integration.
Beyond these traditional functions, these neurons supported dynamic logic operations, including the
exclusive-OR (XOR) function and multiplicative gain modulation, which surpassed the limitations of simple
point neuron models. Fully memristive neural networks, composed of volatile NbOx-based neurons and
nonvolatile TaOx-based synapses, have demonstrated robust pattern recognition capabilities through online
learning and coincidence detection. A Pt/Ti/NbOx/Pt/Ti volatile memristor was placed in series with a load
resistor, while a capacitor was connected in parallel to generate spiking signals . These developments
[79]
highlight the significant potential of memristive technologies in sophisticated and efficient neuromorphic
systems. Furthermore, field-effect transistors with tunable gate voltages could mimic the variable strength of