Effects of Time-Varying Input Signal Stimulation on Artificial Synaptic Transistors

In recent years, artificial synaptic transistors with excellent bionic and energy efficiency have attracted wide attention. However, reports about the synaptic transistors with time-varying input signals are scarce. In this letter, we study the dynamic responses of artificial synaptic transistors to time-varying signals for the first time. We find that the last stimulus has the most significant effect on synaptic weight under the low-voltage stimulus. When considering the presence of a large voltage stimulus in the time-varying input, the closer the time period of the large voltage is to the post-pulse time period, the greater the synaptic weight. Furthermore, we find that the relationship between the time-varying input signal with four pulses and its output result follows the
$2\times2$
multiply-accumulate (MAC) operation rule. This means that the MAC rule has been validated at the individual device level for the first time. This work provides a new temporal synaptic performance and a new testing approach, laying the foundation for the development of neuromorphic computing with temporal signals.