Yang Yihao, Xi Zhongnan, Dong Yuehang, Zheng Chunyan, Hu Haihua, Li Xiaofei, Jiang Zhizheng, Lu Wen-Cai, Wu Di, Wen Zheng
College of Physics and Center for Marine Observation and Communications, Qingdao University, Qingdao 266071, China.
National Laboratory of Solid State Microstructures, Department of Materials Science and Engineering, Jiangsu Key Laboratory of Artificial Functional Materials and Collaborative Innovation Center for Advanced Materials, Nanjing University, Nanjing 210093, China.
ACS Appl Mater Interfaces. 2020 Dec 16;12(50):56300-56309. doi: 10.1021/acsami.0c16385. Epub 2020 Dec 7.
As nanoelectronic synapses, memristive ferroelectric tunnel junctions (FTJs) have triggered great interest due to the potential applications in neuromorphic computing for emulating biological brains. Here, we demonstrate multiferroic FTJ synapses based on the ferroelectric modulation of spin-filtering BaTiO/CoFeO composite barriers. Continuous conductance change with an ON/OFF current ratio of ∼54 400% and long-term memory with the spike-timing-dependent plasticity (STDP) of synaptic weight for Hebbian learning are achieved by controlling the polarization switching of BaTiO. Supervised learning simulations adopting the STDP results as database for weight training are performed on a crossbar neural network and exhibit a high accuracy rate above 97% for recognition. The polarization switching also alters the band alignment of CoFeO barrier relative to the electrodes, giving rise to the change of tunneling magnetoresistance ratio by about 10 times and even the reversal of its sign depending upon the resistance states. These results, especially the electrically switchable spin polarization, provide a new approach toward multiferroic neuromorphic devices with energy-efficient electrical manipulations through potential barrier design. In addition, the availability of spinel ferrite barriers epitaxially grown with ferroelectric oxides also expends the playground of FTJ devices for a broad scope of applications.
作为纳米电子突触，忆阻铁电隧道结（FTJ）因其在模拟生物大脑的神经形态计算中的潜在应用而引发了极大的兴趣。在此，我们展示了基于自旋过滤BaTiO/CoFeO复合势垒的铁电调制的多铁性FTJ突触。通过控制BaTiO的极化切换，实现了开/关电流比约为54400%的连续电导变化以及用于赫布学习的具有突触权重的尖峰时间依赖可塑性（STDP）的长期记忆。采用STDP结果作为权重训练数据库的监督学习模拟在交叉神经网络上进行，识别准确率高于97%。极化切换还改变了CoFeO势垒相对于电极的能带排列，导致隧穿磁电阻比改变约10倍，甚至根据电阻状态使其符号反转。这些结果，特别是电可切换的自旋极化，通过势垒设计为具有节能电操纵的多铁性神经形态器件提供了一种新方法。此外，与铁电氧化物外延生长的尖晶石铁氧体势垒的可用性也扩展了FTJ器件在广泛应用中的范围。
