An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements

Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects. Supplementary Information The online version contains supplementary material available at 10.1007/s40820-022-00875-9.

Characteristics and detail morphologies of the materials. a The field emission scanning electron microscope (FESEM) image of the rare graphite film prepared by 8B pencil. Bottom left inset was the energy spectra of the rare graphite film. The red dotted circles point out areas where graphite is rare on graph paper. b The FESEM image of the medium graphite film prepared by 8B pencil. The corresponding energy spectrum was placed in the bottom left corner. The red dotted circle points out area where graphite is rare on graph paper. c, d The FESEM images of the junction of the rare graphite film and silver conductive film. The rich graphite film was on the left and the silver conductive film was on the right, which showed the clear boundary between the rich graphite film and the silver conductive film. e, f The FESEM images of the junction of the silver conductive film and the graph paper. The silver conductive film was on the left and the graph paper was on the right, which showed the clear boundary between the silver film and the graph paper  The length and the width of the graphite film were 10 mm and 5 mm, respectively. It could be found that the graphite films were resistive-type elements. The resistance of the rare graphite film, medium graphite film, and rich graphite film was 256, 32, and 4 kΩ, respectively. It should be noted that the resistance of the graphite film could be effectively modulated by

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pencil repeatedly drawing on the graph paper and eraser removing some of the graphite. Therefore, the graphite films with different carbon content were designed as the conductive signal transmission channel and the gradient resistance elements of the AIOM touch sensor in a certain resistance range.

Fig. S6
Structure diagram of the AIOM touch sensor. a The stereogram of the breakdown structure of the AIOM touch sensor with seven gradient resistance elements and corresponding seven active touch buttons. b The simplified structure of the AIOM touch sensor in top view. The explanation of the various color symbols was between a and b. c The simplified breakdown structure of the AIOM touch sensor in front section view

Fig. S7
Touch response resistance set of the AIOM touch sensor designed with seven active touch buttons. X-axis from left to right showed none-point touch, one-point touch, two-point touches, three-point touches, four-point touches, five-point touches, six-point touches, and seven-point touches in sequence. In the design scheme, there were a total of 128 response resistance values ranging from 30 to 508 kΩ. Each interval of the response resistance was about 4 kΩ. For one-point touch, there were a total of 7 touch positions and corresponding 7 response resistances. For two-point touches, there were a total of 21 touch combinations and corresponding 21 response resistances. For three-point touches, there were a total of 35 touch combinations and corresponding 35 response resistances. For four-point touches, there were a total of 35 touch combinations and corresponding 35 response resistances. For five-point touches, there were a total of 21 touch combinations and corresponding 21 response resistances. For six-point touches, there were a total of 7 touch combinations and corresponding 7 response resistances. For seven-point touches, there was a total of 1 touch combination and corresponding 1 response resistance. The result indicated that the response resistance was highly regional differentiated to all touch cases. Therefore, the AIOM touch sensor based on the structure of two electrodes and the gradient resistance elements could fully promote wider detection and recognition range of mechanical stimulations and provide richer possibilities and practicability for artificial intelligence-assisted human-machine interactive applications.

Fig. S8
Spatiotemporally dynamic stimulations based on one-point touch and multipoint touches circularly applying on the AIOM touch sensor. Response resistance of the AIOM touch sensor for one dynamic mechanical stimulations and information fusion of dynamic mechanical stimulations based on multipoint touch positions with the different lasting time of a, c 4 s and b, d 1 s. The results demonstrated that the AIOM touch sensor had high robustness and stability in operation. The results further indicated that the mechanosensitive signals were highly regional differentiated to all cases of dynamic mechanical stimulations, and the AIOM touch sensor well realized the establishment and judgment of spatiotemporally dynamic logic with multiple combinations of touches   The user entered the same password "852439" in front of the door access. The key holding time and the interval between keys for the first user were both about 1 s. The key holding time for the second user was about 1 s, and the interval between keys for the second user was about 0.5 s. The key holding time for the third user and the interval between keys for the third user were both approximately 0.5 s

Fig. S13
Accuracy confusion matrix of augmented user verification system based on the Sshaped AIOM touch sensor. We tested the ANN algorithm based on the S-shaped AIOM touch sensor for four different test datasets of user 1, user 2, and user 3, respectively, with a total of 260. Diagrams of a, b, c, and d in the test datasets indicates 90, 100, 70; 90, 90, 80; 60, 100, 100; 100, 70, 90 for user 1, user 2 and user 3, respectively. The confusion matrices (Fig. S13) showed that, during the identification and verification process of the user access control, the ANN algorithm based on the S-shaped AIOM touch sensor achieved an accuracy of 98.1%, 99.2%, 98.8%, 99.2% for the biometrics application with keystroke dynamics respectively. Overall, the confusion matrices indicated that the ANN algorithm achieved an accuracy of over 98% for identification and verification