硅烷化辅助覆盖层层压产生的耐拉伸互连器件,用于智能可贴肤电子设备

IF 10 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY Materials Today Physics Pub Date : 2024-06-29 DOI:10.1016/j.mtphys.2024.101494
Zetao Zheng , Zhuobin Huang , Nian Zhang , Shiyu Liu , Lingyu Zhao , Xingyi Li , Liu Wang , Fang Xu , Jidong Shi
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引用次数: 0

摘要

柔性应变传感器阵列在皮肤监测人体信号和活动方面大有可为。尽管应变敏感材料和图案技术的发展提高了性能和设备集成度,但作为互连器件的金属膜在拉伸时总是很脆弱,从而阻碍了在大应变下的运行。在此,我们开发了一种新策略,用于实现传感器阵列内的抗拉伸互连。通过在沉积银互连器件时引入高模量封盖层,然后在喷墨打印应变敏感石墨烯贴片的可拉伸基板上进行硅烷化辅助层压,银互连器件的变形在很大程度上被抑制在器件的整体应变上,并实现了 40% 的高工作应变范围。此外,封盖层和可拉伸基底之间的化学键确保了电极和敏感层在剧烈弯曲时的稳定接触。所制备的传感器阵列在很宽的范围内(18%)具有很高的灵敏度(GF > 100),可以可靠地监测各种生理信号和人体活动。在传感器阵列和卷积神经网络(CNN)的基础上,开发了机器学习辅助可穿戴手势识别系统,经过 14 次训练后,该系统能以 100% 的准确率区分 10 种定义的手势。这种简便有效的策略可普遍应用于拉伸条件下的金属互连保护,极大地促进了智能柔性电子器件的设计。
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Stretch-tolerant interconnects derived from silanization-assisted capping layer lamination for smart skin-attachable electronics

Flexible strain sensor arrays hold great promise in on-skin monitoring of human signals and activities. Despite the development of strain-sensitive materials and patterning technologies for improved performance and device integration, the metal film serving as interconnects is always vulnerable upon stretch, which hinders the operation under large strains. Herein, a novel strategy is developed for achieving stretch-tolerant interconnects within a sensor array. Through introducing a high-modulus capping layer for the deposition of Ag interconnects, followed by silanization-assisted lamination onto the stretchable substrate where strain-sensitive graphene patches are inkjet-printed, the deformation of Ag interconnects is largely suppressed upon the global strain of the device, and a high working range of 40 % strain is achieved. Moreover, the chemical bonding between the capping layer and the stretchable substrate ensures a stable contact between the electrode and the sensitive layer under vigorous bending. The as-prepared sensor array demonstrates high sensitivity (gauge factor (GF) > 100) within a wide range (18 %), and could reliably monitor various physiological signals and human activities. A machine learning-assisted wearable gesture recognition system is developed based on the sensor array and a convolutional neural network (CNN), which could distinguish from 10 defined gestures with 100 % accuracy after 14 training processes. The facile and effective strategy could be universally applied for metal interconnects protection under stretch, and dramatically facilitate the design of smart flexible electronics.

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来源期刊
Materials Today Physics
Materials Today Physics Materials Science-General Materials Science
CiteScore
14.00
自引率
7.80%
发文量
284
审稿时长
15 days
期刊介绍: Materials Today Physics is a multi-disciplinary journal focused on the physics of materials, encompassing both the physical properties and materials synthesis. Operating at the interface of physics and materials science, this journal covers one of the largest and most dynamic fields within physical science. The forefront research in materials physics is driving advancements in new materials, uncovering new physics, and fostering novel applications at an unprecedented pace.
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