Park Tae Joon, Deng Sunbin, Manna Sukriti, Islam A N M Nafiul, Yu Haoming, Yuan Yifan, Fong Dillon D, Chubykin Alexander A, Sengupta Abhronil, Sankaranarayanan Subramanian K R S, Ramanathan Shriram
School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL, 60439, USA.
Adv Mater. 2023 Sep;35(37):e2203352. doi: 10.1002/adma.202203352. Epub 2022 Nov 30.
The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.
受脑启发的计算、机器人技术,以及更广泛意义上的人工智能(AI)领域,试图将从自然界获取的知识应用于人类设计的电子设备和机器中。在本综述中,我们将讨论复杂氧化物所带来的机遇,复杂氧化物是一类电子陶瓷材料,其性能可通过掺杂、电子相互作用以及在室温附近的各种外部刺激进行精细调节。综述首先从神经系统基本层面的自然智能谈起,接着是由社会互动介导的动物群体层面的集体智能和学习。其中突出强调的一个重要方面是学习和记忆所涉及的巨大时空尺度。然后重点转向集体现象,如金属 - 绝缘体转变(MITs)、铁电性及相关实例,以突出近期人工神经元、突触和电路及其学习方面的演示。随后讨论了电子结构的第一性原理理论处理方法以及运行设备的原位同步辐射光谱学。接着回顾了将实验特性应用于神经网络和算法设计的情况。最后,强调了一些突出的材料挑战,这些挑战需要对物理机制有微观层面的理解,而这对于推进神经形态计算的前沿领域至关重要。