Flexible and rollable MoS ₂ /Si photodetector reveals excellent high flexibility, rollability as well as durability under harsh bending conditions.

Wearable electronic devices with next‐generation biocompatible, mechanical, ultraflexible, and portable sensors are a fast‐growing technology. 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. 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 MoS₂ channel and LiSiO_x (lithium silicate) is demonstrated. Enabled by the sequential fabrication process of all layers, by excluding the transfer process, artificial van der Waals devices combined with the 2D‐MoS₂ channel and LiSiO_x solid electrolyte exhibit excellent neuromorphic synaptic characteristics with a nonlinearity of 0.55 and asymmetry ratio of 0.22. 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. Thus, a high classification accuracy of 95% is achieved without any noticeable performance degradation in the Modified National Institute of Standards and Technology. These results are promising for the development of personalized wearable artificial neural systems in the future.

Sensor-based computing minimizes latency and energy consumption by processing data at the capture point, thereby eliminating extensive data transfer and enabling real-time decision-making. Here, we present a breakthrough in in-sensor computing via circularly polarized light detectors that integrate cholesteric liquid crystal reflectors with two-dimensional van der Waals p-n heterostructures. Our device exhibits a high dissymmetry factor (1.90), allowing effective separation of mixed circularly polarized images, along with a rapid photoresponse (4 μs) and wide linear dynamic range (up to 114.1 dB), suitable for analog multiply-and-accumulate operations in convolution-based in-sensor computing. Harnessing these detectors, we propose mixed-multimodal in-sensor computing using the chiral state of circularly polarized light to dynamically control responsivity, which enables the blending of two arbitrary image processing modes within a single, non-reconfigurable circuit. By effectively integrating polarization-sensitive detectors into the in-sensor computing framework, the proposed architecture preserves kernel optimization capabilities while simplifying circuit complexity.