万青Qing Wan

万青.jpg万青,南京大学电子科学与工程学院教授。2004年本科毕业于浙江大学,2004年博士毕业于中科院上海为系统与信息技术研究所。之后在英国剑桥大学、美国密西根大学和斯坦福大学从事博士后/访问学者科研工作。万青教授的主要研究领域为氧化物半导体及其新概念器件应用。他在NatureCommunications, Adv. Mater., Nano Lett., IEEE EDL APL等杂志上发表了200多篇SCI论文,累计SCI他引1万多次。他先后荣获了万人计划中青年科技领军人才、国家杰出青年基金、教育部自然科学一等奖,浙江省科技一等奖,中国青年科技奖,全国百优博士论文,中科院院长特别奖等荣誉。


Qing Wan is currently a professor in theSchool of Electronic Science and Engineering, Nanjing University, China. He graduated from Zhejiang University in 1998, and received PhD degree from Shanghai Institute of micro-system and Information Technology, Chinese Academy of Sciences in 2004. After that, he worked as a postdoctoral / visiting scholar in Cambridge University, Michigan University and Stanford University. His research focused on the oxide-based semiconductors and new-concept device applications.Qing Wan has published more than 200 scientific papers such as Nature Communications, Adv. Mater., Nano Lett., IEEE EDL and APL. etc., which were SCIcited more than 10000 times. Qing Wan has won many honors, such as the leading young and middle-aged scientific and technological talents of the ten thousand talents program, the national fund for Distinguished Young Scholars, the first prize of natural science of the Ministry of education, the first prize ofscience and technology of Zhejiang Province, the China Youth Science and technology award, the national 100 excellent doctoral dissertation, and the special award of the president of the Chinese Academy of Sciences.



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基于氧化物TFTs的神经形态器件

我们的大脑是一个高度并行、节能和事件驱动的信息处理系统,这与传统的冯诺依曼计算机有着根本的区别。受生物神经计算的启发,神经形态系统可能会为处理模式识别、分类和决策等复杂问题开辟新的范式。虽然两端口忆阻器可以执行一些基本的突触和神经功能,但人类大脑中包含的突触比神经元多得多。这一事实表明,多端口器件更适合于复杂的神经网络仿真。近年来,基于界面离子调制的多端双电层(EDL)薄膜晶体管(TFTs)在模拟突触动态可塑性和神经功能方面受到了广泛关注。早在2009年,我们就发明了一类基于电多端口氧化物双电层TFTs的人工突触/神经元原型器件。该类器件成功模拟了双脉冲易化、枝晶整合和方位定向。此外,我们还对速率编码方案中的神经元增益控制算法进行了实验验证。我们的研究结果为构建类脑认知系统提供了一种新的概念途径。



Oxide-based TFTs for Neuromorphic Devices

Our brain is a highly parallel, energy efficient and event-driven information processing system, which is fundamentally different from traditional von Neumann computers. Inspired by biological neural computing, neuromorphic   systems may open up new paradigms to deal with complicated problems such as pattern recognition, classification and decision making. Although two-terminal memristors can perform some basic synaptic and neural functions, our human brain contains many more synapses than neurons. This fact suggests that multi-terminal devices are more favorable for complex neural network emulation.In recent years, multi-terminal electric-double-layer (EDL) Thin-film transistors (TFTs) based on interfacial ion-modulation have attracted significant attention in mimicking synaptic dynamic plasticity and neuralfunctions. In 2009, we invited a proof-of-principle artificial synapses/neurons based on solid electrolytes coupled oxide-based EDL TFTs with multiple driving and modulatory inputs terminals. Paired-pulse facilitation, dendritic integration and orientation tuning were successfully emulated. Additionally,neuronal gain control (arithmetic) in the scheme of rate coding is alsoexperimentally demonstrated. Our results provide a new-concept approach for building brain-like cognitive systems.