Energy-efficient flexible photoelectric device with 2D/0D hybrid structure for bio-inspired artificial heterosynapse application

The booming artificial intelligence has led to an urgent demand for high-efficient information processing systems. Inspired by the interconnected synapses in human brain, we constructed a novel low-dimensional flexible hybrid photoelectric-modulated artificial heterosynapse with sub-femtojoule energy consumption (0.58 fJ/spike in long-term potentiation (LTP) and 0.86 fJ/spike in long-term depression (LTD)) and ultrafast response (50 ns), which is 105 folds faster than human brain (10 ms). The device synergistically utilizes the remarkable photoelectric properties of a 2D MoSSe channel and a 0D BPQD trap layer to achieve a better computing architecture. The artificial synapse successfully emulates neuromorphic functions under both electric and light stimuli. More importantly, the multi-terminal heterosynaptic plasticity can be modulated effectively by three factors in a cumulative/subtractive way, resembling biological synapses affected by an external neuromodulator, enabling higher order LTP correlations (percentage of increase is similar to 203% compared with electric modulation) and multiple memory states. The performance of the device was unaffected by substrate bending, indicating the robust stability and high flexibility under mechanical strains. Moreover, the Pavlov?s dog classical conditioning experiments were performed to realize associative learning with the synaptic device. These results highlight a new approach for constructing highly efficient wearable neuromorphic computing systems based on mixed lowdimensional structures.

Automated summary

A low-dimensional flexible hybrid photoelectric-modulated artificial heterosynapse was constructed, demonstrating extremely low energy consumption and ultrafast response while successfully emulating neuromorphic functions. The device can effectively modulate the short-term potentiation correlations and multiple memory states of the heterosynapse.