Two-dimensional metal-organic frameworks (2D-MOFs) have been extensively studied as promising materials in the fields of eletrocatalysis, drug delivery, electronic devicese, etc. However, few studies have explored the application potential of 2D-MOFs in novel neuromorphic computing devices. In this work, we report an optoelectronic neuromorphic transistor based on a 2D-MOFs/polymer charge-trapping layer. We found that, the large specific surface area, stable crystal structure, and highly accessible active sites in 2D-MOFs make them excellent charge-trapping materials for our devices, which are beneficial for mimicking the memory and learning functions observed in the organism's nervous systems. Different types of synaptic behaviors have been realized in our 2D-MOFs-based neuromorphic devices under stimuli signal, e.g., paired-pulse facilitation, excitatory post-synaptic current, short-term memory, and long-term memory. More interestingly, emotion-adjustable learning behavior was realized by changing the value of the source-drain voltage. This work can shed light on the application of 2D-MOFs in neuromorphic computing and will contribute to the further development of neuromorphic computing devices.

Corresponding authors Email:  [email protected] (Jia Huang)

                                                                [email protected] (Shilei Dai)