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Sun et al. Soft Sci. 2025, 5, 18 https://dx.doi.org/10.20517/ss.2024.77 Page 21 of 26

scaling these systems to handle the vast amounts of data generated by large-scale sensor arrays remains a
significant hurdle. Future research should focus on optimizing neuromorphic architectures to support high-
density sensor integrations without compromising performance. This includes the development of energy-
efficient processing units, enhanced interconnects, and scalable algorithms capable of managing and
interpreting complex multimodal sensory inputs. Additionally, overcoming miniaturization challenges
through advanced fabrication techniques and the integration of novel materials can lead to more compact
and efficient neuromorphic systems. Incorporating row-column scanning techniques, multifunctional
reconfigurability, large-area sensing arrays, and decoupling algorithms will also be essential in addressing
current limitations. These strategies will enable the creation of robust and versatile neuromorphic
frameworks capable of supporting expansive and intricate sensor networks, ultimately advancing the
capabilities of intelligent, bio-inspired systems.

Furthermore, the development of neuromorphic SOC technologies presents a promising direction by
significantly reducing the area required for computation, lowering power consumption, and enhancing
overall performance. Effective system integration involves the seamless combination of sensors,
computational units, and communication interfaces into a unified platform. This integration is critical for
the functionality and reliability of advanced neuromorphic technologies, as it ensures efficient data
processing and real-time responsiveness. Neuromorphic SOCs can facilitate the compact and efficient
deployment of large-scale sensor arrays by embedding processing capabilities directly within the sensor
framework. This not only minimizes latency, but also reduces power, making the systems more sustainable
and practical for widespread use. Future research should explore novel integration techniques, such as
heterogeneous integration and 3D stacking, to optimize the performance and scalability of neuromorphic
SOCs. One potential solution to enhance SOC performance is the consideration of multimodal sensing and
neuromorphic device integration. By combining these technologies into a unified platform, we can achieve
compact integration of sensor arrays and computational units, improving system functionality and
responsiveness. This integration can be further optimized using laminated electronics, in which the devices
(which cannot be fabricated using traditional CMOS technologies) can be realized by integrating different
layers (wafers) of structures; each layer can be completed with standard CMOS fabrication techniques on a
single wafer. Take energy storage devices as an example, one can first use semiconductor processes to
fabricate electrodes and solid-state electrolyte structures on different wafers, then a novel wafer-scale energy
storage chip can be realized through multi-wafer hybrid integration of the wafers with different structures.
This approach enables the integration of high-performance sensors and processing units to facilitate the
development of advanced multifunctional neuromorphic systems. Additionally, developing standardized
interfaces and modular designs can promote interoperability and ease the integration of diverse
components. By advancing neuromorphic SOC technology, researchers can achieve more compact, energy-
efficient, self-powered, and high-performance intelligent systems, paving the way for their application in
various fields, including healthcare, robotics, and wearable devices.

By addressing these challenges, the integration of skin-inspired sensors with neuromorphic computing can
pave the way for the next generation of intelligent, adaptive systems. These systems will be capable of
sensing, learning, and responding to their environments in ways that closely mimic human perception and
action, thereby enabling more sophisticated and human-like interactions in various applications.

DECLARATIONS
Authors’ contributions
Conducted the literature collection, outlined the manuscript structure, and wrote the manuscript draft: Sun,
J.; Zhang, C.; Yang, C.; Ren, Y.; Ye, T.