Browsing by Author "Liu, Bo"

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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.
A High-Mobility Low-Bandgap Copolymer for Efficient Solar Cells
(2010-12-15)
Ding, Ping
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Chu, Cheng-Che
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Liu, Bo
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Peng, Bo
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Zou, Yingping
A new high-mobility low-bandgap polymer, PBDT-DODTBT, based on benzodithiophene and 5,6-bis(octyloxy)-4,7-di(thiophen-2-yl) benzothiadiazole has been synthesized through a standard Stille coupling reaction. The polymer is soluble in common organic solvents, such as chloroform, tetrahydrofuran, and chlorobenzene and has excellent film forming properties. Preliminary studies of the copolymer showed the charge mobility as high as 7.15 x 10(-3) cm(2).V(-1). s(-1) from SCLC measurement. Initial photovoltaic cells based on the composite structure of ITO/PEDOT: PSS/PBDT-DODTBT: (alpha)PC(71)BM (1:2)/Ca/Al showed an open-circuit voltage of 0.76 V, a power conversion efficiency of 4.02%, and a short-circuit current of 8.96mA.cm(-2).
Bidirectional All-Optical Synapses Based on a 2D Bi2O2Se/Graphene Hybrid Structure for Multifunctional Optoelectronics
(1970-01-01)
Yang, Chia-Ming
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Chen, Tsung-Cheng
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Verma, Dharmendra
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Li, Lain-Jong
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Liu, Bo
Neuromorphic computing has been extensively studied to mimic the brain functions of perception, learning, and memory because it may overcome the von Neumann bottleneck. Here, with the light-induced bidirectional photoresponse of the proposed Bi2O2Se/graphene hybrid structure, its potential use in next-generation neuromorphic hardware is examined with three distinct optoelectronic applications. First, a photodetector based on a Bi2O2Se/graphene hybrid structure presents positive and negative photoresponsibility of 88 and -110 A W-1 achieved by the excitation of visible wavelength and ultraviolet wavelength light at intensities of 1.2 and 0.3 mW cm(-2), respectively. Second, this unique photoresponse contributes to the realization of all optically stimulated long-term potentiation or long-term depression to mimic synaptic short-term plasticity and long-term plasticity, which are attributed to the combined effect of photoconductivity, bolometric, and photoinduced desorption. Third, the devices are applied to perform digital logic functions, such as "AND" and "OR," using full light modulation. The proposed Bi2O2Se/graphene-based optoelectronic device represents an innovative and efficient building block for the development of future multifunctional artificial neuromorphic systems.
Dimensionally anisotropic graphene with high mobility and a high on-off ratio in a three-terminal RRAM device
(2020-06-01)
Liu, Bo
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Tai, Han Hsiang
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Liang, Hanyuan
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Zheng, En-Yan
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Sahoo, Mamina
In the past decade, graphene has aroused worldwide academic passion in the electronics community, due to its unique two dimensional lattice structure and high mobility properties. Meanwhile, the utilization of graphene for next-generation nano-electronic materials is still under debate due to its intrinsic zero band gap nature. To meet the requirement of logic applications, diversified academic attempts have been carried out to modify its intrinsic band structure, such as doping, bilayer stacking, nano-patterning, or heterostructures. In this work, a tailor made strategy for graphene is proposed and experimentally demonstrated: a dimensionally anisotropic graphene based three terminal resistive random-access memory (RRAM). This device could be operated in two modes, which realized not only high carrier mobility (10(3) cm(2) V-1 s(-1)) and low gate leakage in field effect mode, but also a high on-off ratio (10(7)) and ultra-low off state current (1 pA) in resistive switching mode. This device also exhibits high reliability in logic data storage: long retention up to 8000 s, endurance of 400 DC operation cycles, and read immunity up to 1 million cycles. To guarantee the dimensional uniformity and high quality of the graphene, surface polishing and passivation techniques were carried out on Cu foil before graphene CVD synthesis. As demonstrated in this study, the current work paves the way for graphene toward practical logic and memory applications with high performance and low steady state power consumption.
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.