In-memory computing on a photonic platform

Rı́os, Carlos; Youngblood, Nathan; Cheng, Zengguang; Gallo, Manuel Le; Pernice, Wolfram H. P.; Wright, C. David; Sebastian, Abu; Bhaskaran, Harish

doi:10.1126/sciadv.aau5759

Cited by 282 publications

(218 citation statements)

References 49 publications

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“…In addition to the electrical resistance contrast, a pronounced change in the optical property, including refractive index, extinction coefficient, and reflectance, also occurs in PCMs during the phase transition [14,15]. Such features have led to the development of on-chip non-volatile photonic applications, such as nanopixel display [16,17], photonic synapse [18,19], optical computing [20], and optical switching [21][22][23][24].…”

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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“…Non-volatility offers the ability to write and erase information dynamically desirable for large-scale implementations of neuromorphic systems. To that effect, recent demonstrations of sub-ns writing speeds in GST-based PCM technology through optical pulses has opened up a host of opportunities of in-memory computing in the photonic domain [21]. The ultra-fast switching using light overcomes the longstanding obstacle of high 'write' latencies [15] for PCMs in the electrical domain.…”

Section: Introductionmentioning

confidence: 99%

Photonic In-Memory Computing Primitive for Spiking Neural Networks Using Phase-Change Materials

2019

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Spiking Neural Networks (SNNs) offer an event-driven and more biologically realistic alternative to standard Artificial Neural Networks based on analog information processing. This can potentially enable energy-efficient hardware implementations of neuromorphic systems which emulate the functional units of the brain, namely, neurons and synapses. Recent demonstrations of ultra-fast photonic computing devices based on phase-change materials (PCMs) show promise of addressing limitations of electrically driven neuromorphic systems. However, scaling these standalone computing devices to a parallel in-memory computing primitive is a challenge. In this work, we utilize the optical properties of the PCM, Ge2Sb2Te5 (GST), to propose a Photonic Spiking Neural Network computing primitive, comprising of a non-volatile synaptic array integrated seamlessly with previously explored 'integrate-and-fire' neurons. The proposed design realizes an 'in-memory' computing platform that leverages the inherent parallelism of wavelength-division-multiplexing (WDM). We show that the proposed computing platform can be used to emulate a SNN inferencing engine for image classification tasks. The proposed design not only bridges the gap between isolated computing devices and parallel large-scale implementation, but also paves the way for ultra-fast computing and localized on-chip learning.

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“…Importantly, the non-volatile nature our platform, provides an exciting outlook in the development of switchable and reconfigurable metadevices by means of optical or electrical stimuli; enabling novel approaches to switchable metamaterial-based optical components (37)(38)(39).We anticipate that a plethora of novel devices and platforms should arise in the coming years, which will capitalize on the bridge between the electrical and photonic domains that are demonstrated herein. These devices potentially herald true device-level integration of hybrid optoelectronic computing platforms with in-memory computing and multilevel data storage which is readily applicable to this work (40,41).…”

Section: Resultsmentioning

confidence: 99%

Plasmonic nanogap enhanced phase-change devices with dual electrical-optical functionality

et al. 2019

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Modern-day computers use electrical signaling for processing and storing data which is bandwidth limited and power-hungry. These limitations are bypassed in the field of communications, where optical signaling is the norm. To exploit optical signaling in computing, however, new on-chip devices that work seamlessly in both electrical and optical domains are needed. Phase change devices can in principle provide such functionality, but doing so in a single device has proved elusive due to conflicting requirements of size-limited electrical switching and diffraction-limited photonic devices. Here, we combine plasmonics, photonics and electronics to deliver a novel integrated phase-change memory and computing cell that can be electrically or optically switched between binary or multilevel states, and read-out in either mode, thus merging computing and communications technologies.

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In-memory computing on a photonic platform

Abstract: Nonvolatile multilevel phase-change memories on integrated photonic devices enable all-optical direct in-memory multiplications.

Cited by 282 publications

References 49 publications

Miniature Multilevel Optical Memristive Switch Using Phase Change Material

Miniature Multilevel Optical Memristive Switch Using Phase Change Material

Photonic In-Memory Computing Primitive for Spiking Neural Networks Using Phase-Change Materials

Plasmonic nanogap enhanced phase-change devices with dual electrical-optical functionality

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