Time-resolved light-induced insulator-metal transition in niobium dioxide and vanadium dioxide thin films

Beebe, Melissa; Klopf, J. Michael; Wang, Yuhan; Kittiwatanakul, Salinporn; Lu, Jiwei; Wolf, Stuart A.; Lukaszew, R. A.

doi:10.1364/ome.7.000213

Cited by 25 publications

(25 citation statements)

References 32 publications

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“…1(a). The response is similar to that seen when both the pump and probe pulses have a wavelength of 800 nm [19]. An initial strong decrease in the transmittance that recovers after several ps is followed by a long-lived decrease in the transmittance that recovers in several hundred ps to several ns.…”

Section: Resultssupporting

confidence: 64%

“…(1). The ultrafast, short-lived, and long-lived modulations are all observed; however, whereas the short-lived and long-lived modulations correspond to decreases in the transmittance at 1550 nm and the reflectance at 800 nm [19], they represent increases in the reflectance at 1550 nm.…”

Section: Resultsmentioning

confidence: 96%

“…An initial strong decrease in the transmittance that recovers after several ps is followed by a long-lived decrease in the transmittance that recovers in several hundred ps to several ns. To better understand the dynamics of the modulation, the response was fit to a three-exponential decay curve [19],…”

Section: Resultsmentioning

confidence: 99%

“…A thorough understanding of the mechanism of VO 2 's IMT is complicated by the low temperature (340 K) of the IMT [19]. To isolate the optically and thermally induced phenomena, interest has grown in another closely related material, NbO 2 , whose IMT is at a much higher temperature (1081 K).…”

Section: Introductionmentioning

confidence: 99%

“…To isolate the optically and thermally induced phenomena, interest has grown in another closely related material, NbO 2 , whose IMT is at a much higher temperature (1081 K). In the first study of the optically induced IMT of NbO 2 , NbO 2 was pumped by a femtosecond laser pulse at 800 nm and the transient change in the reflectance at 800 nm was measured [19]. The NbO 2 exhibited a transient decrease in reflectance with a recovery described by three time constants: an ultrafast fs response attributed to electron-electron scattering of photoexcited carriers, a short-lived ps response attributed to the recovery of grains of NbO 2 that undergo a purely electronic IMT, and a long-lived ns response attributed to the recovery of grains of NbO 2 that undergo both an electronic IMT and a structural CPT.…”

Section: Introductionmentioning

confidence: 99%

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Thresholdless behavior and linearity of the optically induced metallization of NbO2

Clark

Matsui

et al. 2019

Phys. Rev. Research

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The optical excitation of NbO 2 is known to induce a transient metallicity that partially recovers on a subpicosecond speed and only fully recovers after several nanoseconds. Here, through low-fluence pump-probe spectroscopy, the short-lived and long-lived components of NbO 2 's optically induced modulation are found to be linearly dependent on pump fluence in the low-fluence regime. Through an in-depth analysis, the short-lived and long-lived modulations are found to be caused by photocarrier-induced refractive index changes and photothermally induced changes in the lattice temperature of NbO 2 , respectively, and not an insulator-metal transition.

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Section: Resultssupporting

confidence: 64%

Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Thresholdless behavior and linearity of the optically induced metallization of NbO2

Clark

Matsui

et al. 2019

Phys. Rev. Research

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Essential Characteristics of Memristors for Neuromorphic Computing

Chen

Song

Wang

et al. 2022

Adv Elect Materials

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increasingly problematic. Unlike the Von Neumann computing platform, the human brain relies on neurons and synapses for storage and computation, which do not have clear boundaries between them. Therefore, nanodevices that mimic synapses, for high-efficiency computing, have been investigated; among these nanodevices, memristors have attracted most attention because of their low power consumption, high integration density, and the ability to simulate synaptic plasticity, which meet the standards of neuromorphic computing. [4] The first report on the resistive switching phenomenon dates back to the 1960s; [5] since early theories were insufficient to explain this phenomenon, research had been done on it. It was not until the memristor was theoretically proposed in 1971, that the mechanisms underpinning the resistive switching became abundant. [6] The first memristor was manufactured by Hewlett-Packard in 2008. [7] Since then, memristors made of diverse materials have been successfully studied, including conductive filament memristors, magnetic tunnel junctions, ferroelectric tunnel junctions, phase-change memristors, and so on (Figure 1). These devices have been used for storage and computing purposes. [8,9] In recent years, there have been many reviews investigating neuromorphic computing from the perspectives of device electrical properties, [9,10] resistive switching materials, [11,12] memristive synapses and neurons, [13] algorithm optimization, [14] and circuit design. [15] Different from the existing literature, we discuss the possibility of achieving brain-like computing from the perspective of memristor technology and review the establishment of spiking neural network neuromorphic computing systems. In this article, we first review the resistive switching mechanisms of different types of memristors and focus on factors, which affect device stability and the corresponding optimization measures that have been applied. Furthermore, we study the stochasticity, power consumption, switching speed, retention, endurance, and other properties of memristors, which are the basis for neuromorphic computing implementations. We then review various memristor-based neural networks and the building of spike neural network neuromorphic computing systems. Finally, we shed light upon the major challenges and offer our perspectives and opinions for memristor-based brainlike computing systems.The memristor is a resistive switch where its resistive state is programable based on the applied voltage or current. Memristive devices are thus capable of storing and computing information simultaneously, breaking the Von Neumann bottleneck. Since the first nanomemristor made by Hewlett-Packard in 2008, advances so far have enabled nanostructured, low-power, high-durability devices that exhibit superior performance over conventional CMOS devices. Herein, the development of memristors based on different physical mechanisms is reviewed. In particular, device stability, integration density, power consumption, switching speed, retention, and e...

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