Neuromorphic computing holds immense promise for developing highly efficient computational approaches. Memristor-based artificial neurons, known for due to their straightforward structure, high energy efficiency, and superior scalability, which enable them to successfully mimic biological neurons with electrical devices. However, the reliability of memristors has always been a major obstacle in neuromorphic computing. Here, we propose an ultra-robust and efficient neuron of negative differential resistance (NDR) memristor based on AlAs/In_0.8Ga_0.2As/AlAs quantum well (QW) structure, which has super stable performance such as low variation (0.264%), high temperature resistance (400 °C) and high endurance. The NDR devices can cycle more than 10¹¹ switching cycles at room temperature and more than 10⁹ switching cycles even at a high temperature of 400 °C, which means that the device can operate for more than 310 years at 10 Hz update frequency. Furthermore, the NDR memristor implements the integration feature of the neuronal membrane and avoids using external capacitors, and successfully apply it to the self-designed super reduced neuron circuit. Moreover, we have successfully constructed Fitz Hugh Nagumo (FN) neuron circuit, reduced hardware costs of FN neuron circuit and enabling diverse neuron dynamics and nine neuron functions. Meanwhile, based on the high temperature stability of the device, a voltage-temperature fused multimodal impulse neural network was constructed to achieve 91.74% accuracy in classifying digital images with different temperature labels. This work offers a novel approach to build FN neuron circuits using NDR memristors, and provides a more competitive method to build a highly reliable neuromorphic hardware system.

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