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Flexible Artificial Mechanoreceptor Based on Microwave Annealed Morphotropic Phase Boundary of HfxZr1-xO2 Thin Filmopen accessFlexible Artificial Mechanoreceptor Based on Microwave Annealed Morphotropic Phase Boundary of HfxZr1-xO2 Thin Film

Other Titles
Flexible Artificial Mechanoreceptor Based on Microwave Annealed Morphotropic Phase Boundary of HfxZr1-xO2 Thin Film
Authors
Jung, MinhyunKim, SeungyeobHwang, JunghyeonKim, Hye JinKim, YunjeongAhn, JinhoJeon, Sanghun
Issue Date
Feb-2024
Publisher
Wiley-VCH Verlag
Keywords
artificial mechanoreceptor; E-skin; flexible electronics; HfZrO; microwave annealing
Citation
Advanced Electronic Materials, v.10, no.2, pp 1 - 9
Pages
9
Indexed
SCIE
SCOPUS
Journal Title
Advanced Electronic Materials
Volume
10
Number
2
Start Page
1
End Page
9
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/196911
DOI
10.1002/aelm.202300594
ISSN
2199-160X
2199-160X
Abstract
The development of artificial tactile receptor systems is important in the fields of prosthetic devices, interfaces for the metaverse, and sensors. A pressure sensor and memory device may be used in this system to replicate the tactile detecting capabilities of human skin. The implementation of systems that take into account mass production and miniaturization is still difficult. Here, a flexible artificial tactile receptor built using conventional semiconductor processes that combine a vertically stacked piezoelectric sensor with neuromorphic memory is presented. As a fundamental component for both sensors and memory, hafnium zirconium oxide (HZO) formed by using semiconductor deposition technique is introduced. Due to its exceptional piezoelectric performance, the morphotropic phase boundary of HZO is studied. The entire materials and processes are highly compatible with conventional semiconductor processes, including microwave annealing-based low-temperature crystallization. Even after 10,000 times of bending stress, the sensor and memory constructed on a flexible substrate exhibit consistent pressure detection characteristics over a wide range of 2-25 kPa. The feasibility of the approach is further demonstrated by a deep neural network simulation, which reached 90.8% braille recognition accuracy. Wearable electronics and medical devices are two examples of industrial domains that can use these flexible, exceptionally durable devices.
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