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Real-time sensing and processing of tactile information are essential to enhance the capability of artificial electronic skins (e-skins), enabling unprecedented intelligent applications in tactile exploration and object manipulation. However, conventional tactile e-skin systems typically execute redundant data transfer and conversion for decision making due to their physical separation between sensors and processing units, leading to high transmission latency and power consumption. Here, we report an in-sensor tactile computing system based on a flexible capacitive pressure sensor array. This system utilizes multiple connected sensor networks to execute in-situ analog multiplication and accumulation operations, achieving both tactile sensing and computing functionalities. We experimentally implemented the in-sensor tactile computing system for low-level tactile sensory processing tasks including noise reduction and edge detection. The consumed power for single sensing-computing operation is over 22 times lower than that of a conventional mixed electronic system. These results demonstrate that our capacitive in-sensor computing system paves a promising way for power-constrained applications such as robotics and human-machine interfaces.
Traditional tactile systems suffer from the physical separation between sensing and processing units, causing latency and power issues. Here, Chen et al. report a capacitive in-sensor tactile computing system, using sensor networks to execute in-situ multiplication and accumulation operations.
Details
Pressure sensors;
Computation;
Electronic systems;
Noise reduction;
Electrodes;
Separation;
Accumulation;
Polyvinyl alcohol;
Power consumption;
Robotics;
Man-machine interfaces;
Neural networks;
Sensors;
Sensor arrays;
Power management;
Information processing;
Latency;
Tactile sensors (robotics);
Sensory integration;
Decision making;
Edge detection
; Qiu, Jie 1
; Yang, Dongzi 3 ; Liu, Mengyang 2 ; Zhang, Mengru 3 ; Li, Chenyang 1 ; Wu, Zhongyuan 4 ; Yu, Jie 2 ; Zhang, Xumeng 2
; Chen, Xianzhe 2
; Huang, Zhangcheng 2
; Song, Enming 4
; Wang, Ming 5
; Liu, Qi 5
; Liu, Ming 5 1 Fudan University, State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Zhangjiang Fudan International Innovation Center, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443); Fudan University, College of Integrated Circuits and Micro-Nano Electronics, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443); Zhangjiang Laboratory, Shanghai, China (GRID:grid.8547.e)
2 Fudan University, State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Zhangjiang Fudan International Innovation Center, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443)
3 Fudan University, State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Zhangjiang Fudan International Innovation Center, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443); Fudan University, College of Integrated Circuits and Micro-Nano Electronics, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443)
4 Fudan University, Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443)
5 Fudan University, State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Zhangjiang Fudan International Innovation Center, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443); Zhangjiang Laboratory, Shanghai, China (GRID:grid.8547.e)