![]() The 4th industrial age, characterized by the Internet of Things and artificial intelligence (AI), has led to a substantial increase in data generation across electronic devices with a projected 170 (zettabyte) to be generated in 2025. These results are promising for the development of personalized wearable artificial neural systems in the future. ![]() Thus, a high classification accuracy of 95% is achieved without any noticeable performance degradation in the Modified National Institute of Standards and Technology. Based on the excellent flexibility of colorless polyimide substrates and thin-layered structures, the fabricated flexible neuromorphic synaptic devices exhibit superior long-term potentiation and long-term depression cyclic endurance performance, even when bent over 700 times or on curved surfaces with a diameter of 10 mm. Enabled by the sequential fabrication process of all layers, by excluding the transfer process, artificial van der Waals devices combined with the 2D-MoS 2 channel and LiSiO x solid electrolyte exhibit excellent neuromorphic synaptic characteristics with a nonlinearity of 0.55 and asymmetry ratio of 0.22. Herein, the development of an ultraflexible artificial-synaptic array device with concrete-mechanical cyclic endurance consisting of a novel heterostructure with an all-solid-state 2D MoS 2 channel and LiSiO x (lithium silicate) is demonstrated. Hardware systems enabling artificial neural networks while consuming low power and processing massive in situ personal data are essential for adaptive wearable neuromorphic edging computing. ![]() Wearable electronic devices with next-generation biocompatible, mechanical, ultraflexible, and portable sensors are a fast-growing technology.
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