Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
Hebei Key Laboratory of Optic-Electronic Information Materials, College of Physics Science and Technology, Hebei University, Baoding 071002, People's Republic of China.
Mater Horiz. 2023 Oct 2;10(10):4521-4531. doi: 10.1039/d3mh00835e.
By mimicking the behavior of the human brain, artificial neural systems offer the possibility to further improve computing efficiency and solve the von Neumann bottleneck. In particular, neural systems with perceptual capability expand the application field and lay a good foundation for the construction of perceptual storage and computational systems. However, research on neurons with perceptual functions is still relatively scarce, with most works focusing on optoelectronic synapses. The neuron is important for neuromorphic computing systems because neurons output excitatory or inhibitory stimuli to regulate the weight of synapses. Therefore, the construction of sensory neurons is crucial to expand the application range of brain-like neural computing. Here, an artificial sensory neuron is proposed, which is constructed using a photosensitive bipolar threshold switching memristor based on NdNiO (NNO) nanocrystals. These metallic phase nanocrystals can not only enhance the local electric field, but also act as a reservoir for defects (VS) to guide the growth of conductive filaments and stabilize the performance of the device. They present stable bipolar threshold switching behavior with a low 120 nW set power, and the operating voltages decreased in light due to photocarrier action. A leaky integrate firing (LIF) neuron has been realized, which achieved key biological neuron functions, such as all-or-nothing spiking, threshold-driven firing, refractory period, and spiking frequency modulation. The LIF neurons receiving optical inputs have the properties of an artificial sensory neuron. It could regulate the spiking output frequency at different light densities, which could be used for a ship approaching a port. This work provides a promising hardware implementation towards constructing high-performance artificial intelligence to assist ships at night in a sensory system.
通过模拟人脑的行为，人工神经网络提供了进一步提高计算效率和解决冯·诺依曼瓶颈的可能性。特别是具有感知能力的神经网络扩展了应用领域，为感知存储和计算系统的构建奠定了良好的基础。然而，具有感知功能的神经元的研究仍然相对较少，大多数工作都集中在光电突触上。神经元对于神经形态计算系统非常重要，因为神经元输出兴奋性或抑制性刺激来调节突触的权重。因此，构建感觉神经元对于扩展类脑神经计算的应用范围至关重要。在这里，提出了一种人工感觉神经元，它是使用基于 NdNiO (NNO) 纳米晶体的光敏双极阈值开关忆阻器构建的。这些金属相纳米晶体不仅可以增强局部电场，还可以作为缺陷 (VS) 的储库来引导导电丝的生长并稳定器件的性能。它们呈现出稳定的双极阈值开关行为，具有低至 120nW 的设定功率，并且由于光生载流子的作用，工作电压随光照而降低。已经实现了漏积分触发 (LIF) 神经元，其实现了关键的生物神经元功能，如全有或全无的尖峰、阈值驱动的触发、不应期和尖峰频率调制。接收光输入的 LIF 神经元具有人工感觉神经元的特性。它可以根据不同的光密度调节尖峰输出频率，这可用于船只接近港口的情况。这项工作为构建高性能人工智能提供了有前途的硬件实现，以辅助船只在夜间的感测系统。
