Elements of Nanophotonics

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Caulfield, J. Kinser, and S. IEEE 77, — Snoek, H. Larochelle, and R. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Cai and V. Khoram, A. Chen, D.

Liu, Q. Wang, Z. Yu, and L. Chen, S. Jayasuriya, J. Stephen, S. Sivaramakrishnan, A. Veeraraghavan, and A. Jing, Y. Shen, T. Dubcek, J. Peurifoy, S. Skirlo, Y. LeCun, M. Tegmark, and M. Hermans and T. Nair and G.

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Joannopoulos, S. Johnson, J. Winn, and R. Prucnal and B. Saxena and J. Citing articles from OSA journals and other participating publishers are listed here. Alert me when this article is cited. Click here to see a list of articles that cite this paper. The input wave encodes the image as the intensity distribution. As can be seen, although the field distributions differ for the images of the same digit, they are classified as the same digit. This step is followed by an iterative process to solve for the electric field in a nonlinear medium.

Next, we use the ASM to calculate the gradient, which is then used to update the level-set function and consequently, the medium itself. Here we use mini-batch SGD explained in the supplementary materials section of Ref. In training with mini-batches, we sum the cost functions calculated for different images in the same batch and compute the gradients.

After iteration 66, the medium has already seen each of the training samples at least once, since we are using batches of images. At each step, the boundary between the host material and the inclusions is shown, along with the field distribution for the same randomly selected digit 8. Also, the accuracy of the medium on the test set can be seen for that particular stage in training. Different colors illustrate varying values of permittivity. The input image is projected onto the top surface. Computing is performed while the wave propagates through the 3D medium.

The field distribution on the bottom surface is used to recognize the image. Full-wave simulation shows the optical energy is concentrated on the location with the correct class label, in this case 6.

The rows on the matrix show true labels of the images that have been presented as input, and the columns depict the labels that the medium has classified each input. Therefore, the diagonal elements show the number of correct classifications out of every 10 samples see Visualization 2. Learn more. Login or Create Account.

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Allow All Cookies. Photonics Research Vol. Accessible Open Access. Abstract Full Article Figures 4 Suppl. Abstract We show optical waves passing through a nanophotonic medium can perform artificial neural computing. Artificial neural networks using complex numbers and phase encoded weights Howard E. Design and optimization of optical passive elements using artificial neural networks Ahmed M. Training of photonic neural networks through in situ backpropagation and gradient measurement Tyler W. More Recommended Articles. Color image identification and reconstruction using artificial neural networks on multimode fiber images: towards an all-optical design Nadav Shabairou, Eyal Cohen, Omer Wagner, Dror Malka, and Zeev Zalevsky Opt.

Adams, R. ACS Photon. Figures 4. Passive neural computing is performed by light passing through the nanostructured medium with both linear and nonlinear scatterers. It is a nonlinear function of the incident wave intensity. This material is used as nonlinear activation, as indicated by light blue color. Equations 5 Equations on this page are rendered with MathJax.

nanoHUB-U Nanophotonic Modeling L1.1: Photonic Bandstructures and Bandgaps: Introduction

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CRC Halbleiter-Nanophotonik: CRC

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Integrated Photonics Research, Silicon and Nanophotonics

Remember me on this computer. Login Cancel. OpenAthens Login. OSA Privacy Policy. Need help? On the edge between nano and micro, the development of efficient coherent sources for terahertz THz emission is a subject of intense activity for both applied and fundamental research.

The terahertz frequency range of the electromagnetic spectrum, in the far infrared, has a broad number of potential applications across the physical, medical, biological, environmental and astronomical sciences.

Integrated Photonics Research, Silicon and Nanophotonics

Another topic of interest lies with the strong coupling regime between electronic transitions and THz photons confined in semiconductor micro-cavities. Fees Information. To Top.