The parameters were obtained from the fitting curves, which would be applied in the following MNIST-based recognition simulation process. The recognition rate of the network was increased with the input process of training images (Figure \ref{366439}c). The network under high VDS (positive emotion) exhibits a higher recognition rate (~75%) than that under low VDS (negative emotion, ~65%). The instability of the training process can be observed in a low-VDS (negative emotion) network. Figure \ref{366439}d illustrates the mapping of the corresponding conductances (W), which can recognize the digit “7” before and after training. It can be observed that the conductance mapping based on high VDS (positive emotion) exhibits the more obvious pattern “7” than that based on low VDS (negative emotion).

Conclusion

In this work, we have successfully demonstrated a novel optoelectronic neuromorphic transistor based on the design of the tunable energy band structure of the 2D MOFs-polymer/OSC layer. The 2D MOFs-polymer blended layer was used as the photo-sensing component and the uniformly dispersed 2D MOFs were used as the charge trapping centers. The generation, transportation, and trapping processes of the photogenerated charges on the 2D MOFs-polymer/OSC heterojunction provided the transistors with a variety of synaptic behaviors. More interestingly, we have successfully simulated human emotion-tunable learning and memory behaviors via changing the value of source-drain voltage. The study can shed light on the application of 2D-MOFs in neuromorphic computing and is also helpful to the further development of neuromorphic computing devices.

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2021YFA1101303), the Science and Technology Foundation of Shanghai (19JC1412402 and 20JC1415600), the National Natural Science Foundation of China (62074111, 81870824), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100), Shanghai Municipal Commission of Science and Technology Project (19511132101), Shanghai Rising-Star Program(20QA1407800), and the support of the Fundamental Research Funds for the Central Universities. The authors are also thankful for the support of the Measurements and Analysis Center, School of Materials Science and Engineering, Tongji University.

Conflict of interest

The authors declare no conflict of interests.