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Home
Massively Parallel In-sensor Skinomorphic Computing.

Home
Massively Parallel In-sensor Skinomorphic Computing.

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Massively parallel in-sensor skinomorphic computing.

Yixiang Li¹, Yuekun Yang^1,2, Cong Wang^1,2

¹Institute of Brain-inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Jiangsu Physical Science Research Center, Nanjing University, Nanjing, China.

Nature Communications

|April 8, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel in-sensor computing scheme for skinomorphic electronics, enabling real-time tactile sensing and processing. This breakthrough advances intelligent robotics and wearable technology by integrating computation directly within sensors.

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Area of Science:

Materials Science
Robotics
Electronics Engineering

Background:

Traditional tactile sensing separates sensors and processors, hindering real-time processing for skinomorphic electronics.
This limitation poses a significant challenge for the advancement of intelligent robotics and wearable technology.

Purpose of the Study:

To propose and demonstrate a massively parallel in-sensor skinomorphic computing scheme for tactile perception.
To overcome the limitations of traditional tactile sensing by enabling parallel sensing and processing directly within the sensor.

Main Methods:

Fabrication of a 32x32 flexible capacitive pressure sensor array with high uniformity and endurance.
Integration of the sensor array with a memristive crossbar array for in-sensor computing.

Experimental demonstration of parallel sensing, restoration of pressure patterns, and feature extraction.

Main Results:

Successfully demonstrated parallel sensing and restoration of broken pressure patterns (e.g., 'NJU').
Achieved direct, parallel extraction of textural features from complex pressure patterns using networked sensor and memristive arrays.
Showcased tactile information compression through parallel feature extraction.

Conclusions:

The proposed in-sensor computing scheme enables real-time, high-throughput tactile perception for intelligent skins.
This approach overcomes the physical separation bottleneck in traditional tactile sensing.
Opens new possibilities for advanced skinomorphic electronics in robotics and wearables.