Xue Fei, He Xin, Wang Zhenyu, Retamal José Ramón Durán, Chai Zheng, Jing Lingling, Zhang Chenhui, Fang Hui, Chai Yang, Jiang Tao, Zhang Weidong, Alshareef Husam N, Ji Zhigang, Li Lain-Jong, He Jr-Hau, Zhang Xixiang
Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia.
National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China.
Adv Mater. 2021 May;33(21):e2008709. doi: 10.1002/adma.202008709. Epub 2021 Apr 15.
Ferroelectrics have been demonstrated as excellent building blocks for high-performance nonvolatile memories, including memristors, which play critical roles in the hardware implementation of artificial synapses and in-memory computing. Here, it is reported that the emerging van der Waals ferroelectric α-In Se can be used to successfully implement heterosynaptic plasticity (a fundamental but rarely emulated synaptic form) and achieve a resistance-switching ratio of heterosynaptic memristors above 10 , which is two orders of magnitude larger than that in other similar devices. The polarization change of ferroelectric α-In Se channel is responsible for the resistance switching at various paired terminals. The third terminal of α-In Se memristors exhibits nonvolatile control over channel current at a picoampere level, endowing the devices with picojoule read-energy consumption to emulate the associative heterosynaptic learning. The simulation proves that both supervised and unsupervised learning manners can be implemented in α-In Se neutral networks with high image recognition accuracy. Moreover, these heterosynaptic devices can naturally realize Boolean logic without an additional circuit component. The results suggest that van der Waals ferroelectrics hold great potential for applications in complex, energy-efficient, brain-inspired computing systems and logic-in-memory computers.
铁电体已被证明是高性能非易失性存储器的优秀构建模块,包括忆阻器,它们在人工突触的硬件实现和内存计算中发挥着关键作用。在此,据报道,新兴的范德华铁电体α-In Se可用于成功实现异突触可塑性(一种基本但很少被模拟的突触形式),并使异突触忆阻器的电阻切换比超过10,这比其他类似器件大两个数量级。铁电体α-In Se通道的极化变化是不同配对端电阻切换的原因。α-In Se忆阻器的第三端在皮安级对通道电流表现出非易失性控制,使器件具有皮焦耳的读取能耗,以模拟关联异突触学习。模拟证明,监督学习和无监督学习方式都可以在具有高图像识别准确率的α-In Se神经网络中实现。此外,这些异突触器件无需额外的电路组件就能自然实现布尔逻辑。结果表明范德华铁电体在复杂、节能、受大脑启发的计算系统和内存逻辑计算机中具有巨大的应用潜力。