Loss and coupling tuning via heterogeneous integration of MoS<sub>2</sub> layers in silicon photonics [Invited]

Maiti, Rishi; Patil, Chandraman; Hemnani, Rohit; Miscuglio, Mario; Amin, Rubab; Ma, Zhizhen; Chaudhary, Rimjhim; Johnson, A. T. Charlie; Bartels, Ludwig; Agarwal, Ritesh; Sorger, Volker J.

doi:10.1364/ome.9.000751

Cited by 35 publications

(14 citation statements)

References 38 publications

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“…4) [8]. To our knowledge this is the first time that either device is demonstrated in such a platform, however Lipson's Group recently demonstrated strong phase-modulation in an MZI structure [9]. The EOM is realized in a two-terminal in-plane electrode configuration where 2D hBN flakes are used as gate diele ctric, and MoS 2 as the actively gated material ( Fig.…”

Section: Recent Heterogeneous Si-photonic-integrated Modulator Demonsmentioning

confidence: 98%

A Guide for Material and Design Choices for Electro-Optic Modulators

Amin¹,

Zhizhen²,

Tahersima³

et al. 2019

Optical Fiber Communication Conference (OFC) 2019

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Electro-optic modulation performs a technological relevant functionality such as for communication, beam steering, or neuromorphic computing through providing the nonlinear activation function of a perceptron. Wile Silicon photonics enabled the integration and hence miniaturization of optoelectronic devices, the weak electro-optic performance of Silicon renders these modulators to be bulky and power-hungry compared to a single switch functionality known from electronics. To gain deeper insights into the physics and operation of modulators heterogenerous integration of emerging electro-optically active materials could enable separating light passive and low-loss light routing from active light manipulation. Here we discuss and review our recent work on a) fundamental performance vectors of electro-optic modulators, and b) showcase recent development of heterogeneous-integrated emerging EO materials into Si-photonics to include an ITO-based MZM, a Graphene hybrid-plasmon and the first TMD-MRR modulator using a microring resonator. Our results indicate a viable path for energy efficient and compactSilicon photonic based modulators.

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Section: Recent Heterogeneous Si-photonic-integrated Modulator Demonsmentioning

confidence: 98%

A Guide for Material and Design Choices for Electro-Optic Modulators

Amin¹,

Zhizhen²,

Tahersima³

et al. 2019

Optical Fiber Communication Conference (OFC) 2019

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“…The resonance shift with graphene potential is due to the change in the effective mode index. The resonace shift with graphene coverage and potential is calculated as [27]:

n_{r} = \frac{()(, 2 π R - L_{g}) \times n_{w} + L_{g} \times n_{μ}}{2 π R}

normalΔ λ = \frac{normalΔ n_{r} \times λ_{r}}{n_{w}}

where R is the radius of the ring and L g is the graphene coverage length. The effective index for the bare ( n w ) and graphene covered waveguide ( n µ ) at different potential is calculated choosing the TE‐like mode.…”

Section: Analysis Of Loss and Coupling Coefficient Of Graphene Intementioning

confidence: 99%

Field enhancement assisted graphene‐based microring modulator for high modulation depth

Joshi

Kaushik

2020

IET Optoelectronics

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“…Hence, many research groups have strived for engineering on-chip modulators beyond the solutions offered by heterogeneous integrated photonic-foundries to date. Recent developments of monolithically and CMOS compatible integrated emerging EO or EA materials, such as Indium Tin Oxide (ITO) 15,57 , graphene 14,18,58,59 , quantum-confined structures 60 and TMDs into Si-photonics with specific device configuration aiming to enhance mode overlap allowed energy efficient 61,62 , compact silicon photonic based modulators. The important performance metric for EOMs include high ER (>3dB), low IL (<1dB), modulation speed (>25 GHz), low energy consumption per bit (<10fJ/bit), and compact footprint area (possibly 3D volume).…”

Section: Electro-optic 'Weights' and Nonlinear Activation Function: Mmentioning

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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Loss and coupling tuning via heterogeneous integration of MoS₂ layers in silicon photonics [Invited]

Cited by 35 publications

References 38 publications

A Guide for Material and Design Choices for Electro-Optic Modulators

A Guide for Material and Design Choices for Electro-Optic Modulators

Field enhancement assisted graphene‐based microring modulator for high modulation depth

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

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Loss and coupling tuning via heterogeneous integration of MoS2 layers in silicon photonics [Invited]

Cited by 35 publications

References 38 publications

A Guide for Material and Design Choices for Electro-Optic Modulators

A Guide for Material and Design Choices for Electro-Optic Modulators

Field enhancement assisted graphene‐based microring modulator for high modulation depth

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

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Product

Resources

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Loss and coupling tuning via heterogeneous integration of MoS₂ layers in silicon photonics [Invited]