Temperature Regulated Artificial Neuron Based on Memristor

Abstract

Recently, artificial neurons have attracted much attention due to their excellent energy efficiency and scalability. However, there are still few reports on the regulation of the performance of artificial neuron based on single device. In the letter, we present an artificial neuron device based on Ag/TaOx/Si that not only has excellent turnon and turn-off performance, but also exhibits sustained stability under multiple cycle tests. Moreover, the Integrateand-Fire (IF) neuron model is successfully simulated at room temperature. More importantly, we find that an increase of temperature could reduce the threshold voltage and reset voltage of neurons, improve the probability of neuronal firing, and promote the transformation of neurons from nonvolatile to volatile. This work provides an effective method for regulating artificial neurons and has important implications for studying information transfer in biology and adapting complex computations in a memory computing framework.

Advances in pixel driving technology for micro-LED displays

“…There are many ways to prepare memristors, and the memristors stacked by Ag/TaO x /Si exhibit excellent performance. 99 As shown in Fig. 9.…”

“…If the memristor is scanned using a negative DC from 0 V to −5 V at this time, the memristor can be reset to the LRS to complete a cycle. Wu et al 99 did an even better job in this regard, as shown in Fig. 10.…”

“…The effect of temperature on the memristor performance is critical. Wu et al 99 demonstrated that the pulse required by the synapse to generate the peak current slowly decreases as the temperature gradually increases. When the temperature reaches 65 °C, the memristor converts from non-volatile to volatile.…”

N-Doped Graphene/MXene Nanocomposite as a Temperature-Adaptive Neuromorphic Memristor

“…11,12 Research progresses on temperature-adaptive, low-power electronic synaptic memristors, enhancing performance and reliability for AI and neuromorphic computing. 13,14 Evidently, recent reports in the study of temperature adaptive amnesia have reported explorations from the material design, device physics, and application levels, suggesting that the high-temperature performance of amnesia is important and reliable in neuromorphic computation and that the development of it has some potential applications in the power electronics and aerospace industries. Sato et al explored widebandgap material design for memristors applied at high temperatures.…”

“…In integrated circuit applications, erase and write processes lead to elevated device temperature and power consumption, impacting the reliability and lifespan. An adaptive low-power mode post-warm-up operation is suggested to maintain synaptic learning behavior while minimizing energy consumption. , Research progresses on temperature-adaptive, low-power electronic synaptic memristors, enhancing performance and reliability for AI and neuromorphic computing. , …”