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Table 1. Performance overview of on-chip neuromorphic computing system integrated with skin-inspired neuromorphic sensors
Skin-inspired Power Response
neuromorphic Materials Type of task consumption time Sensitivity/accuracy Ref.
sensors
5
-1
Piezoresistive pressure Nafion/PDMS/gold- Gesture recognition 10-200 pJ 86 ms 3.8 × 10 kPa /N/A [114]
sensor with Nafion-based coated micropyramids
memristor
-1
Triboelectric sensors FEP/PI Cardiac sounds sensing Self-powered ≈ ms 1,215 mV·Pa /97% [115]
and diagnosis of heart
diseases
TENG Ecoflex rubber/etched Cardiovascular activity Self-powered ≈ ms N/A/99.73% [116]
copper foil monitoring
Two-dimensional PENGs CNFs/PDMS Human motion N/A ≈ ms N/A/93.75% [117]
monitoring
t-TENGs CNFs/PDMS Human motion N/A ≈ ms N/A/93.43% [118]
monitoring
Piezoresistive sensors CNFs/PDMS Tactile sensing, gesture N/A 100 ms N/A [119]
recognition
-1
Piezoresistive sensors CNF/PAN/PDMS Human motion N/A ≈ ms 1.82 kN /N/A [120]
monitoring
Skin-inspired tactile PVDF-TrFE/AgNW Material identification Self-powered ≈ ms N/A [121]
sensor
HPPMS ZnO NWs/MoO Force sensing, image N/A ≈ ms N/A [122]
3
recognition
Triboelectric-capacitive- Liquid-metal-based Multichannel tactile N/A 6 ms N/A/100% [64]
coupled sensing
tactile sensor

PDMS: Polydimethylsiloxane; N/A: not available; FEP: fluorinated ethylene propylene; PI: polyimide; TENG: triboelectric nanogenerator; PENGs:
piezoelectric nanogenerators; CNFs: carbon nanofibers; t-TENGs: textile triboelectric nanogenerators; PAN: polyacrylonitrile; PVDF-TrFE:
poly(vinylidene fluoride-co-trifluoroethylene); AgNW: silver nanowire; HPPMS: high-resolution pressure piezo-memory system; NWs: nanowires.

CONCLUSION AND OUTLOOK
Skin-inspired neuromorphic sensors showed great potential to revolutionize future robotics, healthcare,
wearables, and smart textiles. The advantages of these systems, such as real-time responsiveness,
adaptability, and energy efficiency, make this research area highly promising. Although significant progress
has been made, some challenges remain in this research field, such as the scaling of sensor arrays, signal
interference, neuromorphic SOC technologies, etc. These obstacles need to be addressed for the broader
implementation of e-skin systems.

To tackle scaling and signal interference issues, future research should prioritize specific strategies aimed at
enhancing sensor accuracy and the scalability of multimodal sensor arrays. One promising direction
involves the development of advanced materials, such as flexible conductive polymers, dielectric elastomers,
and novel nanomaterials. These materials can help create large-scale, flexible sensor arrays with high
sensitivity and reliability. Furthermore, innovations in nanotechnology and 3D printing hold the potential
to enable the scalable production of these sensor arrays, ensuring that they can conform seamlessly to a
variety of surfaces while maintaining performance. Additionally, novel hybrid sensor modalities, where
different sensors complement each other to detect various signals, can be designed and implemented to
overcome signal interference. Such an approach will improve the overall reliability and accuracy of the
sensor networks.

Parallel to sensor advancements, refining neuromorphic computational frameworks and addressing
scalability and miniaturization challenges are pivotal for the progression of these technologies.
Neuromorphic systems aim to emulate the efficiency and adaptability of biological neural networks, yet