Browsing by Author "Liu, Zhiwei"

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A Fluorographene-Based Synaptic Transistor
(2019-08-13)
Liu, Bo
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Hong, Ming-Chun
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Sahoo, Mamina
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Ong, Bin Leong
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Tok, Eng Soon
Exploring brain-inspired synaptic devices has recently become a new focus of research in nanoelectronic communities. In this emerging field, incorporating 2D materials into three-terminal synaptic transistors has brought various advantages. However, achieving a stable and long-term weight-modulation in these synaptic transistors, which are typically based on interface charge storage, is still a challenge due to the nature of their spontaneous relaxation. The application of an atomically thin fluorographene layer into the synaptic junction region suppresses this issue and improves the efficiency, tunability, and symmetry of the synaptic plasticity as well as establishing a stable weight-regulation paradigm. These unique properties can be attributed to the dipolar rotation of C-F in fluorographene. To obtain a better physical understanding, a vacancy-dependent C-F dipolar rotation model is proposed and supported by hysteresis analysis and density functional theory calculations. As proposed and demonstrated, the unique fluorographene-based synaptic transistor may be a promising building block for constructing efficient neuromorphic computing hardware.
Failure Analysis of Gate-all-around Nanowire Field Effect Transistor Under TLP Test
(2014)
Zhang, Guoyan
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Dong, Aihua
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Liu, Nie
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Tian, Rui
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Yang, Xuejiao
Electrostatic discharge (ESD) characters of Nanowire Field Effect Transistors have been tested and analyzed in detail in this paper. TLP (transmission line pulsing technique) and semiconductor characterization system have been used for experiments. The failure currents and leakage currents of Nanowire Field Effect Transistor are characterized. Also, physical insights and failure model are provided to analyze the failure mechanism.
Programmable Synaptic Metaplasticity and below Femtojoule Spiking Energy Realized in Graphene-Based Neuromorphic Memristor
(2018-06-20)
Liu, Bo
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Liu, Zhiwei
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Chiu, In-Shiang
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Di, MengFu
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Wu, YongRen
Memristors with rich interior dynamics of ion migration are promising for mimicking various biological synaptic functions in neuromorphic hardware systems. A graphene-based memristor shows an extremely low energy consumption of less than a femtojoule per spike, by taking advantage of weak surface van der Waals interaction of graphene. The device also shows an intriguing programmable metaplasticity property in which the synaptic plasticity depends on the history of the stimuli and yet allows rapid reconfiguration via an immediate stimulus. This graphene-based memristor could be a promising building block toward designing highly versatile and extremely energy efficient neuromorphic computing systems.