Optical memristive switches

Koch, Ueli; Hoessbacher, Claudia; Emboras, Alexandros; Leuthold, Juerg

doi:10.1007/s10832-017-0072-3

Cited by 43 publications

(31 citation statements)

References 79 publications

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“…Optical memristive switches are digital optical switches with multiple different transmission states that can be stored for long periods without external voltage. Hence, it possesses characteristics from both an optical switch and an optical memory in one configuration [25,26]. Optical memristive switches are particularly interesting for use as a new kind of optical fieldprogrammable devices whose optical transfer functions can be electrically written and erased.…”

Section: Introductionmentioning

confidence: 99%

Miniature Multilevel Optical Memristive Switch Using Phase Change Material

et al. 2019

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The optical memristive switches are electrically activated optical switches that can memorize the current state. They can be used as optical latching switches in which the switching state is changed only by applying an electrical Write/Erase pulse and maintained without external power supply.We demonstrate an optical memristive switch based on a silicon MMI structure covered with nanoscale-size Ge2Sb2Te5 (GST) material on top. The phase change of GST is triggered by resistive heating of the silicon layer beneath GST with an electrical pulse. Experimental results reveal that the optical transmissivity can be tuned in a controllable and repeatable manner with the maximum transmission contrast exceeding 20 dB. Partial crystallization of GST is obtained by controlling the width and amplitude of the electric pulses. Crucially, we also demonstrate that both Erase and Write operations, to and from any intermediate level, are possible with accurate control of the electrical pulses. Our work marks a significant step forward towards realizing photonic memristive switches without static power consumption, which are highly demanded in highdensity large-scale integrated photonics.

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Section: Introductionmentioning

confidence: 99%

Miniature Multilevel Optical Memristive Switch Using Phase Change Material

et al. 2019

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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

174

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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“…[152][153][154] Various optoelectronic synaptic devices based on different 2D layered materials and architectures have been proposed to emulate bio-synaptic features. Similarly, light stimulus can also be used as signal input to tune and control behaviors of the optoelectronic synaptic devices through modifying their electrical conductance.…”

Section: Optoelectronic Synaptic Devicesmentioning

confidence: 99%

2D Layered Materials for Memristive and Neuromorphic Applications

Wang

Meng

et al. 2019

Adv Elect Materials

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demonstrated that the memristive devices exhibit many promising features, [3][4][5][6][7][8] such as non-volatility, high speed switching, high endurance, high-density integration, CMOS-compatible fabrication, and so on. These features make them ideal candidates for applications in memory devices, [3,5,9] in-memory computing, [3,[10][11][12] and edge computing. [13,14] The working principle of the TMOs-based memristive devices relies on ion drift or diffusion, which resembles motion of ions in the biological neurons and synapses. The ionic motions exhibit dynamic behaviors. Thus, the memristive devices can be used to emulate the synaptic plasticity [15] and neurons. [16][17][18] Compared to traditional silicon synapses [19][20][21] and neurons [22,23] requiring complex circuits, such emerging memristive devices have compact structures and enhanced func-

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Optical memristive switches

Cited by 43 publications

References 79 publications

Miniature Multilevel Optical Memristive Switch Using Phase Change Material

Miniature Multilevel Optical Memristive Switch Using Phase Change Material

Memristive Synapses and Neurons for Bioinspired Computing

2D Layered Materials for Memristive and Neuromorphic Applications

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