Electro-optical synaptic characteristics of ferroelectric liquid crystals for artificial intelligence

We propose electro-optical synaptic devices using surface-stabilized ferroelectric liquid crystals. Typical synaptic characteristics were observed for varying pulse time intervals, numbers of pulses, and signal voltages. Plasticity only occurred when pulses were applied at intervals shorter than the response time of the ferroelectric liquid crystal. Moreover, the plasticity increased with a higher pulse voltage and number of pulses. This demonstrates the importance of repeated learning. The synaptic weights required to make connections through learning in an artificial neural network can be determined by tuning the pulse signal. We discuss the high-speed computational potential of optical neuromorphic devices using liquid crystals.