scholarly journals Photoreduced nanocomposites of graphene oxide/N-doped carbon dots toward all-carbon memristive synapses

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Ya Lin ◽  
Zhongqiang Wang ◽  
Xue Zhang ◽  
Tao Zeng ◽  
Liang Bai ◽  
...  

Abstract An all-carbon memristive synapse is highly desirable for hardware implementation in future wearable neuromorphic computing systems. Graphene oxide (GO) can exhibit resistive switching (RS) and may be a feasible candidate to achieve this objective. However, the digital-type RS often occurring in GO-based memristors restricts the biorealistic emulation of synaptic functions. Here, an all-carbon memristive synapse with analog-type RS behavior was demonstrated through photoreduction of GO and N-doped carbon quantum dot (NCQD) nanocomposites. Ultraviolet light irradiation induced the local reduction of GO near the NCQDs, therefore forming multiple weak conductive filaments and demonstrating analog RS with a continuous conductance change. This analog RS enabled the close emulation of several essential synaptic plasticity behaviors; more importantly, the high linearity of the conductance change also facilitated the implementation of pattern recognition with high accuracy. Furthermore, the all-carbon memristive synapse can be transferred onto diverse substrates, showing good flexibility and 3D conformality. Memristive potentiation/depression was stably performed at 450 K, indicating the resistance of the synapse to high temperature. The photoreduction method provides a new path for the fabrication of all-carbon memristive synapses, which supports the development of wearable neuromorphic electronics.

Nanomaterials ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 560
Author(s):  
Alexandra Carvalho ◽  
Mariana C. F. Costa ◽  
Valeria S. Marangoni ◽  
Pei Rou Ng ◽  
Thi Le Hang Nguyen ◽  
...  

We show that the degree of oxidation of graphene oxide (GO) can be obtained by using a combination of state-of-the-art ab initio computational modeling and X-ray photoemission spectroscopy (XPS). We show that the shift of the XPS C1s peak relative to pristine graphene, ΔEC1s, can be described with high accuracy by ΔEC1s=A(cO−cl)2+E0, where c0 is the oxygen concentration, A=52.3 eV, cl=0.122, and E0=1.22 eV. Our results demonstrate a precise determination of the oxygen content of GO samples.


2019 ◽  
Vol 153 ◽  
pp. 79-83 ◽  
Author(s):  
Wooseok Choi ◽  
Kibong Moon ◽  
Myonghoon Kwak ◽  
Changhyuck Sung ◽  
Jongwon Lee ◽  
...  

Nanoscale ◽  
2019 ◽  
Vol 11 (38) ◽  
pp. 17590-17599 ◽  
Author(s):  
Nian Duan ◽  
Yi Li ◽  
Hsiao-Cheng Chiang ◽  
Jia Chen ◽  
Wen-Qian Pan ◽  
...  

An electro-photo-sensitive synapse based on a highly reliable InGaZnO thin-film transistor is demonstrated to mimic synaptic functions and pattern-recognition functions.


1996 ◽  
Author(s):  
Nickolay N. Evtikhiev ◽  
Boris N. Onyky ◽  
Dmitry V. Repin ◽  
Igor B. Scherbakov ◽  
Rostislav S. Starikov ◽  
...  

2019 ◽  
Vol 3 (1) ◽  
pp. 9-19 ◽  
Author(s):  
Fazal Noor

Ultrasonic sensors have been used in a variety of applications to measure ranges to objects. Hand gestures via ultrasonic sensors form unique motion patterns for controls. In this research, patterns formed by placing a set of objects in a grid of cells are used for control purposes. A neural network algorithm is implemented on a microcontroller which takes in range signals as inputs read from ultrasonic sensors and classifies them in one of four classes. The neural network is then trained to classify patterns based on objects’ locations in real-time. The testing of the neural network for pattern recognition is performed on a testbed consisting of Inter-Integrated Circuit (I2C) ultrasonic sensors and a microcontroller. The performance of the proposed model is presented and it is observed the model is highly scalable, accurate, robust and reliable for applications requiring high accuracy such as in robotics and artificial intelligence.


2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Dongshin Kim ◽  
Jang-Sik Lee

Abstract Emulating neurons/synapses in the brain is an important step to realizing highly efficient computers. This fact makes neuromorphic devices important emerging solutions to the limitations imposed by the current computing architecture. To mimic synaptic functions in the brain, it is critical to replicate ionic movements in the nervous system. It is therefore important to note that ions move easily in liquids. In this study, we demonstrate a liquid-based neuromorphic device that is capable of mimicking the movement of ions in the nervous system by controlling Na+ movement in an aqueous solution. The concentration of Na+ in the solution can control the ionic conductivity of the device. The device shows short-term and long-term plasticity such as excitatory postsynaptic current, paired-pulse facilitation, potentiation, and depression, which are key properties for memorization and computation in the brain. This device has the potential to overcome the limitations of current von Neumann architecture-based computing systems and substantially advance the technology of neuromorphic computing.


PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e69237 ◽  
Author(s):  
Andre F. Marquand ◽  
Maurizio Filippone ◽  
John Ashburner ◽  
Mark Girolami ◽  
Janaina Mourao-Miranda ◽  
...  

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