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.

中文翻译：

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源自硅烷化辅助覆盖层层压的耐拉伸互连件，用于智能皮肤附着电子产品


柔性应变传感器阵列在人体信号和活动的皮肤监测方面具有广阔的前景。尽管应变敏感材料和图案化技术的发展以提高性能和器件集成度，但用作互连的金属膜在拉伸时总是很脆弱，这阻碍了大应变下的操作。在此，开发了一种新颖的策略来在传感器阵列内实现耐拉伸互连。通过引入高模量覆盖层来沉积银互连，然后在可拉伸基板上进行硅烷化辅助层压，其中应变敏感石墨烯片是喷墨印刷的，银互连的变形在很大程度上抑制了银互连的整体应变。装置，并实现了 40% 应变的高工作范围。此外，覆盖层和可拉伸基材之间的化学键合确保了电极和敏感层在剧烈弯曲下的稳定接触。所制备的传感器阵列在较宽的范围（18％）内表现出高灵敏度（计量因子（GF）％3E 100），并且可以可靠地监测各种生理信号和人体活动。基于传感器阵列和卷积神经网络（CNN）开发了机器学习辅助的可穿戴手势识别系统，经过14次训练过程后，可以以100%的准确率区分10个定义的手势。这种简便有效的策略可以普遍应用于拉伸下的金属互连保护，并极大地促进智能柔性电子产品的设计。