An Optoelectronic Resistive Switching Memory with Integrated Demodulating and Arithmetic Functions

Tan, Hongwei; Liu, Gang; Zhu, Xiaojian; Yang, Huali; Chen, Bin; Chen, Xinxin; Shang, Jie; Lü, Wei; Wu, Yihong; Li, Run Wei

doi:10.1002/adma.201500039

Cited by 197 publications

(176 citation statements)

References 44 publications

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“…Although there were quite large variations in the estimated current values, all the memory cells show a drastic decrease in current, suggesting that the trapped electrons are detrapped and the memory cells reset. 29 Three cells show similar current levels to HRS, but four other cells show a much lower current. These suggest that the initial state already contains a certain number of electrons trapped at the trap sites.…”

Section: Resultsmentioning

confidence: 91%

Electronic resistance switching in the Al/TiO_x/Al structure for forming-free and area-scalable memory

et al. 2015

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Electronic bipolar resistance switching (eBRS) in an Al/TiOx/Al structure, where the TiOx layer was reactively sputter-deposited, was examined in conjunction with a structural analysis using transmission electron microscopy. A thin (3-5 nm) insulating Al(Ti)Ox layer was formed at the bottom Al electrode interface, which provided the necessary asymmetric potential barrier for the eBRS to emerge, whereas the top Al electrode interface appeared to have provided the fluent carrier (electron) injection. The set and reset switching were related to the trapping and detrapping of the carriers at the trap centers, the characteristic energy of which was ∼0.86 eV, across the entire electrode area. The general features of this material system as the feasible RS memory were insufficient: endurance cycle, <∼8000, and retention time at 85 °C, 10(6) s. However, the detailed analysis of the switching behavior based on the space-charge limited current conduction mechanism, and its variation with the switching cycles, provided useful information on the general features of the eBRS, which could also be applicable to other binary (or even ternary) metal-oxide RS systems based on the electronic switching mechanism.

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Section: Resultsmentioning

confidence: 91%

Electronic resistance switching in the Al/TiO_x/Al structure for forming-free and area-scalable memory

et al. 2015

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“…Arithmetic operations were also demonstrated in Ref. [46] with an opto-electronic resistive switching memory. However, light pulses with widths of seconds were used and a linear dependency between the current of the device and the pulse number was shown.…”

Section: Arithmetic Computingmentioning

confidence: 98%

Electro-Photo-Sensitive Memristor for Neuromorphic and Arithmetic Computing

et al. 2016

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We present optically and electrically tunable conductance modifications of a site-controlled quantum dot memristor. The conductance of the device is tuned by electron localization on a quantum dot. The control of the conductance with voltage and low power light pulses enables applications in neuromorphic and arithmetic computing. As in neural networks, applying pre-and post-synaptic voltage pulses to the memristor allows to increase (potentiation) or decrease (depression) the conductance by tuning the time difference between the electrical pulses. Exploiting state-dependent thresholds for potentiation and depression, we were able to demonstrate a memory-dependent induction of learning. The discharging of the quantum dot can further be induced by low power light pulses in the nW-range. In combination with the state-dependent threshold voltage for discharging, this enables applications as generic building blocks to perform arithmetic operations in bases ranging from binary to decimal with low power optical excitation. Our findings allow the realization of optoelectronic memristor-based synapses in artificial neural networks with a memory-dependent induction of learning and enhanced functionality by performing arithmetic operations.

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“…In fact, two live cortical neurons were coupled through organic memristive synapses to realize synchronized delta-oscillation in the two-neuron network. Some impressive and encouraging optical memristive devices were developed in the last couple of years; [235][236][237][238][239][240][241][242][243] however, the techniques are still far from practical applications. Accordingly, the switching mechanisms should be further investigated for new emerging memristive materials.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

188

127

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and constant data movement are needed while working. In the human brain, huge and complex neural networks composed of gigantic amounts of neurons massively interconnected by an even larger number of synapses are in charge of computing and memory. Unlike a digital computer, information storage and processing happen at the same time in the brain. [3] Artificial intelligence (AI) that could rival biological neural networks is being continuously pursued by human being. There are two approaches to implement AI: one is the software simulation of neural networks with the aid of digital computers, another is developing hardware-integrated circuits of neural networks. In fact, AI based on the software simulation is evolving very fast thanks to the advances in the development of algorithm in recent years, and it even outperforms the human brain in specific complex tasks, such as playing the game of Go. [4] However, software-based AI suffers from critical issues of enormous power consumption and massive data throughput. Hardware-based AI systems are more efficient in terms of speed and power consumption; however, complementary metal oxide semiconductor (CMOS) transistors are inherently different from the basic building blocks of biological neural networks, neurons, and synapses, in terms of operation dynamic and behavior. A circuit consisting of at least ten transistors are required to realize the function of one biological synapse, which is impractical for the implementation of large networks, not to mention the human brain (roughly 10 11 neurons interconnected by ≈10 15 synapses). [5,6] Fortunately, the emergence of memristive devices with innate dynamics resembling biological synapses and neurons provides a feasible and simple way to build hardware-based bio-inspired computing systems. [7,8] For instance, only one compact memristive device with a metalinsulator-metal (MIM) sandwich structure is sufficient to reproduce some functions of a biological synapse. Moreover, the vector-matrix multiplication, which is believed to be the most data-intensive work in neural networks, can be naturally performed in a cross-bar array of memristive devices on the physical level based on Ohm and Kirchhoff laws. [9,10] These features endow the memristive devices ideally suited to realize highly efficient bioinspired computing systems in hardware.To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state-of-art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biore...

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An Optoelectronic Resistive Switching Memory with Integrated Demodulating and Arithmetic Functions

Cited by 197 publications

References 44 publications

Electronic resistance switching in the Al/TiO_x/Al structure for forming-free and area-scalable memory

Electronic resistance switching in the Al/TiO_x/Al structure for forming-free and area-scalable memory

Electro-Photo-Sensitive Memristor for Neuromorphic and Arithmetic Computing

Memristive Synapses and Neurons for Bioinspired Computing

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An Optoelectronic Resistive Switching Memory with Integrated Demodulating and Arithmetic Functions

Cited by 197 publications

References 44 publications

Electronic resistance switching in the Al/TiOx/Al structure for forming-free and area-scalable memory

Electronic resistance switching in the Al/TiOx/Al structure for forming-free and area-scalable memory

Electro-Photo-Sensitive Memristor for Neuromorphic and Arithmetic Computing

Memristive Synapses and Neurons for Bioinspired Computing

Contact Info

Product

Resources

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Electronic resistance switching in the Al/TiO_x/Al structure for forming-free and area-scalable memory

Electronic resistance switching in the Al/TiO_x/Al structure for forming-free and area-scalable memory