Nonlinear optical components for all-optical probabilistic graphical model

Babaeian, Masoud; Blanche, Pierre-A.; Norwood, Robert A.; Kaplas, Tommi; Keiffer, Patrick; Svirko, Yuri; Allen, Taylor G.; Chen, Vincent W.; Chi, San‐Hui; Perry, Joseph W.; Marder, Seth R.; Neifeld, Mark A.; Peyghambarian, N.

doi:10.1038/s41467-018-04578-x

Cited by 13 publications

(13 citation statements)

References 47 publications

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“…This amorphous pyrolyzed photoresist film (PPF) can be transformed into a graphitic PPF (GrPPF), which possesses improved -in comparison with the PPF -crystallinity. This advance can be very useful, for instance, in graphene enhanced Raman spectroscopy [22] or in optical computing [24]. It is worth noting that although the presented technique utilizes photoresist as a carbon precursor, we believe that other polymer materials [27] or pyrolytic carbon [25,26] may also be considered as precursors.…”

Section: Introductionmentioning

confidence: 95%

Strong optical nonlinearity of ultrathin graphitic films synthesized on dielectric substrates

Kaplas

Babaeian

Cromey

et al. 2019

Applied Surface Science

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We propose and demonstrate a scalable technique to grow a thin polycrystalline graphitic film directly onto a fused silica substrate. The technique is based on the pyrolysis of a photoresist in the presence of a sacrificial 10 nm thick nickel catalyst layer. The synthesized graphitic film with a thickness of about 50 nm possesses almost constant 40% absorptance over visual and near infrared spectral regions. By using Raman characterization, third harmonic generation spectroscopy, and the Z-scan technique we perform a comparative study of the films pyrolyzed with and without a Ni catalyst. We show that the amorphous carbon dominates the linear and nonlinear optical properties of the resist film pyrolyzed without the Ni catalyst. In contrast, in presence of a Ni catalyst layer, the pyrolysis leads to a graphitic film that demonstrates a strong saturable absorption behavior at 1550 nm wavelength and has a nonlinear refractive index comparable with that of graphene. Thus, the developed, transfer-free synthesis technique provides an alternative route towards the controllable growth of wafer scale graphitic films on the dielectric substrates for photonics applications.

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Section: Introductionmentioning

confidence: 95%

Strong optical nonlinearity of ultrathin graphitic films synthesized on dielectric substrates

Kaplas

Babaeian

Cromey

et al. 2019

Applied Surface Science

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“…Indeed, a complex extension of ReLU, ModReLU, has been proposed [28]. While it is physically unrealistic to implement ModReLU, the nonoptimality of Softplus functions still motivates the exploration of other optical nonlinearities, such as optical bistability in microring resonators [29], and two-photon absorption [30,31] as alternatives. 3 Neural network architecture and software implementation…”

Section: Physical Design Of Optical Neural Networkmentioning

confidence: 99%

Design of optical neural networks with component imprecisions

Fang¹,

Manipatruni²,

Wierzynski³

et al. 2019

Opt. Express

158

100

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For the benefit of designing scalable, fault resistant optical neural networks (ONNs), we investigate the effects architectural designs have on the ONNs' robustness to imprecise components. We train two ONNs -one with a more tunable design (GridNet) and one with better fault tolerance (FFTNet) -to classify handwritten digits. When simulated without any imperfections, GridNet yields a better accuracy (∼ 98%) than FFTNet (∼ 95%). However, under a small amount of error in their photonic components, the more fault tolerant FFTNet overtakes GridNet. We further provide thorough quantitative and qualitative analyses of ONNs' sensitivity to varying levels and types of imprecisions. Our results offer guidelines for the principled design of fault-tolerant ONNs as well as a foundation for further research.Second, rather than optimization towards a specific matrix, the linear operations learned for the classification task is not, a priori, known. As such, our primary figure of merit is the classification accuracy instead of the fidelity between the target unitary matrix and the one learned.Lastly, the aforementioned studies mainly focused on the optimization of the networks after fabrication. The imprecisions introduced generally reduced the expressivity of the network -how well the network can represent arbitrary transformations. Evaluation of this reduction in tunability and mitigating strategies were provided. However, such post-fabrication optimization requires the characterization of every MZI, the number of which scales with the dimension (N ) of the network as N 2 . Protocols for self configuration of imprecise photonic networks have been demonstrated [17,18]. While measurement of MZIs were not necessary in such protocols, each MZI needed to be configured progressively and sequentially. Thus, the same N 2 scaling problem remained. Furthermore, if multiple ONN devices are fabricated, each device, with unique imperfections, has to be optimized separately. The total computational power required, therefore, scales with the number of devices produced.In contrast, we consider the effects of imprecisions introduced after software training of ONNs (Code 1, Ref.[19]), details of which we present in Sec. 3. This pre-fabrication training is more scalable, both in network size and fabrication volume. An ideal ONN (i.e., one with no imprecisions) is trained in software only once and the parameters are transferred to multiple fabricated instances of the network with imprecise components. No subsequent characterization or tuning of devices are necessary. In addition to the benefit of better scalability, fabrication of static MZIs can be made more precise and cost effective compared to re-configurable ones.We evaluate the degradation of ONNs from their ideal performances with increasing imprecision. To understand how such effects can be minimized, we investigate the role that the architectural designs have on ONNs' sensitivity to imprecisions. The results are presented in Sec. 4.1. Specifically, we study the performance of two ONNs i...

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“…However, when it comes to intricate problems such as optimization dealing with very large data sets, even breakthrough application-specific integrated circuit technologies such as a tensor processing unit have shown substantial limitations in practice [1]. All-optical computing holds the promise to solve challenging problems such as large-scale combinatorial optimizations [2,3] and probabilistic graphical models [4], which have found many practical applications in artificial intelligence, image processing, and social networks.…”

Section: Introductionmentioning

confidence: 99%

Meta-optics for spatial optical analog computing

2020

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Rapidly growing demands for high-performance computing, powerful data processing, and big data necessitate the advent of novel optical devices to perform demanding computing processes effectively. Due to its unprecedented growth in the past two decades, the field of meta-optics offers a viable solution for spatially, spectrally, and/or even temporally sculpting amplitude, phase, polarization, and/or dispersion of optical wavefronts. In this review, we discuss state-of-the-art developments, as well as emerging trends, in computational metastructures as disruptive platforms for spatial optical analog computation. Two fundamental approaches based on general concepts of spatial Fourier transformation and Green’s function (GF) are discussed in detail. Moreover, numerical investigations and experimental demonstrations of computational optical surfaces and metastructures for solving a diverse set of mathematical problems (e.g., integrodifferentiation and convolution equations) necessary for on-demand information processing (e.g., edge detection) are reviewed. Finally, we explore the current challenges and the potential resolutions in computational meta-optics followed by our perspective on future research directions and possible developments in this promising area.

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Nonlinear optical components for all-optical probabilistic graphical model

Cited by 13 publications

References 47 publications

Strong optical nonlinearity of ultrathin graphitic films synthesized on dielectric substrates

Strong optical nonlinearity of ultrathin graphitic films synthesized on dielectric substrates

Design of optical neural networks with component imprecisions

Meta-optics for spatial optical analog computing

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