Silicon Nitride Photonic Integration Platforms for Visible, Near-Infrared and Mid-Infrared Applications

Muñoz, Pascual; Micó, Gloria; Bru, Luis A.; Pastor, D.; Pérez, Daniel; Doménech, José David; Fernández, Juan Pêdro Solano; Baños, Rocío; Gargallo, Bernardo; Alemany, Rubén; Sánchez, Ana; Cirera, Josep M.; Mas, Roser; Domı́nguez, Carlos

doi:10.3390/s17092088

Cited by 216 publications

(140 citation statements)

References 63 publications

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“…Driven by these applications, several materials have been investigated as candidates for visible photonics platform, including SiO 2 [22,23], Si 3 N 4 [24][25][26][27], diamond [28][29][30][31][32], TiO 2 [33] and AlN [34]. With exception of AlN, all of these platforms are electro-optically passive and do not allow for fast control of optical signals.…”

Section: Introductionmentioning

confidence: 99%

Ultra-low-loss integrated visible photonics using thin-film lithium niobate

Desiatov

Shams-Ansari

Zhang

et al. 2019

Optica

207

151

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Integrated photonics is a powerful platform that can improve the performance and stability of optical systems, while providing low-cost, small-footprint and scalable alternatives to implementations based on free-space optics. While great progress has been made on the development of low-loss integrated photonics platforms at telecom wavelengths, visible wavelength range has received less attention. Yet, many applications utilize visible or near-visible light, including those in optical imaging, optogenetics, and quantum science and technology. Here we demonstrate an ultra-low loss integrated visible photonics platform based on thin film lithium niobate on insulator. Our waveguides feature ultra-low propagation loss of 6 dB/m, while our microring resonators have an intrinsic quality factor of 11 million, both measured at 637 nm wavelength. Additionally, we demonstrate an on-chip visible intensity modulator with an electro-optic bandwidth of 10 GHz, limited by the detector used. The ultra-low loss devices demonstrated in this work, together with the strong secondand third-order nonlinearities in lithium niobate, open up new opportunities for creating novel passive, and active devices for frequency metrology and quantum information processing in the visible spectrum range. I. 1. INTRODUCTIONLow-loss, active and integrated photonic platform operating at visible wavelengths is of great interest for applications ranging from quantum optics and metrology to bio-sensing and bio-medicine. For example, alkali and alkaline earth metals such as rubidium, cesium, calcium and sodium, the key elements for modern precision optical frequency metrology [1-3], magnetometry [4-6] and quantum computation [7-10], have their atomic transitions in visible and near-visible spectrum range. In addition, integrated photonic circuits at visible wavelengths found their way into the fields such as optogenetics [11,12] and bio-sensing [13][14][15]. Furthermore, visible wavelength light is used for quantum state preparation [16], manipulation and read out of color centers [17], quantum dots [18,19] and various quantum emitters in 2d materials [20,21]. Driven by these applications, several materials have been investigated as candidates for visible photonics platform, including SiO 2 [22,23], Si 3 N 4 [24-27], diamond [28-32], TiO 2 [33] and AlN [34]. With exception of AlN, all of these platforms are electro-optically passive and do not allow for fast control of optical signals. Here we show that lithium niobate (LN) is a promising integrated platform for visible photonics, owing to its wide transparency window (400 nm -5000 nm), and large electro-optic coefficient, ∼30 times larger than that of AlN, and strong optical nonlinearity [35]. Our work builds on recently developed thin film lithium niobate (TFLN) substrates [36] and the novel fabrication method [37] which enabled realization of high-performance electro-optical (EO) modulators [38][39][40] and Kerr and EO frequency combs [41,42] in telecom wavelength range (1500-1650nm). TFLN platform has...

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Section: Introductionmentioning

confidence: 99%

Ultra-low-loss integrated visible photonics using thin-film lithium niobate

Desiatov

Shams-Ansari

Zhang

et al. 2019

Optica

207

151

View full text Add to dashboard Cite

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“…The IMB-CNM is a moderate confinement SiN photonics integration platform aiming at covering a wavelength range from 400-3700 nm for photonic integrated applications such as biophotonics, tele/datacom and sensing (TRL≥7) [76]. The photonic platform runs in the Integrated Micro-and Nanotechnology clean room hosted by the IMB-CNM (CSIC) parallel to a standard CMOS line.…”

Section: E Imb-cnm Sin Platformmentioning

confidence: 99%

Open-Access Silicon Photonics Platforms in Europe

Rahim

Goyvaerts

Szelag

et al. 2019

IEEE J. Select. Topics Quantum Electron.

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Offering open-access silicon photonics-based technologies has played a pivotal role in unleashing this technology from research laboratories to industry. Fabless enterprises rely on the open-access of these technologies for their product development. In the last decade, a diverse set of open-access technologies with medium and high technology readiness levels have emerged. This paper provides a review of the open-access silicon and silicon nitride photonic IC technologies offered by the pilot lines of European research institutes and companies. The

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“…Interestingly, the process recipes for silicon nitride are favorable for large-scale photonic networks; when deposited by LPCVD Si3N4 is characterized by both high material stability and refractive index regularity, as well as nanoscale etch-resolution and lower surface roughness, which leads to reduced scattering and hence low optical losses per unit length (<10 dB/m) for a 0.5 mm bending radii, which is 10-100x lower compared to SOI. 40 In contrast to SOI, due to the fundamental flexibility of the deposition techniques 41 , Si3N4 also allows for easy integration and 3D stacking flexibility with either SOI or CMOS platforms 42 . Regarding other material integration into this silicon nitride platform, several options have been successfully embedded such as a number of metals or colloidal quantum dots.…”

Section: Passive Photonic Neural Network Interconnectivity: Waveguidementioning

confidence: 99%

Roadmap on material-function mapping for photonic-electronic hybrid neural networks

et al. 2019

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Driven by machine-learning tasks neural networks have demonstrated useful capabilities as nonlinear hypothesis classifiers. The underlying technologies performing the dotproduct multiplication, the summation, and the nonlinear thresholding on the input data in electronics, however, are limited by the same capacitive challenges known from electronic integrated circuits. The optical domain, in contrast, provides low delay interconnectivity suitable for such node-distributed non-Von Neumann architectures relying on dense node-to-node communication. Thus, once the neural network's weights are set, the delay of the network is just given by the time-of-flight of the photon, which is in the picosecond range for photonic integrated circuits. However, the functionality of memory for storing the trained weights does not exists in optics, thus demanding a fresh look to explore synergies between photonics and electronics in neural networks. Here we provide a roadmap to pave the way for emerging hybridized photonic-electronic neural networks by taking a detailed look into a single node's perceptron, discussing how it can be realized in hybrid photonic-electronic heterogeneous technologies. We show that a set of materials exist that exploit synergies with respect to a number of constrains including electronic contacts, memory functionality, electro-optic modulation, optical nonlinearity, and device packaging. We find that the material ITO, in particular, could provide a viable path for both the perceptron 'weights' and the nonlinear activation function, while simultaneously being a foundry process-near material. We finally identify a number of challenges that, if solved, could accelerate the adoption of such heterogeneous integration strategies of emerging memory materials into integrated photonics platforms for real-time responsive neural networks. Introduction:While the scientific community still lacks a full understanding of the operation of the human brain, we yet can draw some parallels to compute-systems with respect to operating efficiency. Machine learning (ML) tasks performed by neural networks (NN) can be used for such computer-vs.-brain comparison, since both are examples of nonlinear hypothesis systems that can be trained to classify patterns. Interestingly, one of the fastest computers 1 are only able to simulate ~1% of human brain activity in requiring about one hour demanding massive compute infrastructure overheads (e.g. 82,000 processors, and 1.73 billion virtual nerve cells connected by 10.4 trillion synapses) consuming about 10 megawatts, while the human brain operates on 10's of Watts 2 . In the aim to compensate this disparity and to match some of the brain's computational and energy efficiency, the recent efforts to develop non-Von Neumann neuromorphic hardware are capable of efficiently implementing artificial NN operations, in particular vector matrix multiplication (VMM) and backpropagation 3-5 . However, unlike

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Silicon Nitride Photonic Integration Platforms for Visible, Near-Infrared and Mid-Infrared Applications

Cited by 216 publications

References 63 publications

Ultra-low-loss integrated visible photonics using thin-film lithium niobate

Ultra-low-loss integrated visible photonics using thin-film lithium niobate

Open-Access Silicon Photonics Platforms in Europe

Roadmap on material-function mapping for photonic-electronic hybrid neural networks

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