Modern electronics based on semiconductors is meeting the fundamental speed limit caused by the interconnect delay and large heat generation when the sizes of components reach nanometer scale. Photons as a carrier of the information are superior to electrons in bandwidth, density, speed, and dissipation. More over, photons could carry intensity, polarization, phase, and frequency information which could break through the limitation of binary system as in electronic devices. But due to the diffraction limitation, the photonic components and devices can not be fabricated small enough to be integrated densely. Surface plasmon polariton is quanta of collective oscillations of free electrons excited by photons in metal nanostrucrures, which offers a promising way to manipulate light at the nanoscale and to realize the miniaturization of photonic devices. Hence, plasmonic circuits and devices have been proposed for some time as a potential strategy for advancing semiconductor-based computing beyond the fundamental performance limitations of electronic devices, as epitomized by Moore's law. A variety of individual plasmonic nanodevices have been intensively studied recently, but the crucial and necessary step to enable nanophotonic circuits for future information technology, namely cascade logics integrated on-chip, has not been achieved. Here we demonstrate that a nanophotonic binary logic NOR gate can be realized by cascading plasmonic OR and NOT gates in four-terminal nanowire networks. We explain the operating principle for the device based on quantum dot luminescence imaging, which reveal the interferences for different logic functions between propagating plasmon wave packets in the nanowire network in great detail. Since the NOR gate is logic complete, i.e. any Boolean logic gate can be constructed from it, our results could have a key role in defining a viable path for the development of novel subwavelength optical processor architectures.

Cascaded Logic Gates in Nanophotonic Plasmon Networks

Matrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms. Photonic accelerators are designed to accelerate specific categories of computing in the optical domain, especially matrix multiplication, to address the growing demand for computing resources and capacity. Photonic matrix multiplication has much potential to expand the domain of telecommunication, and artificial intelligence benefiting from its superior performance. Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors. In this review, we first introduce the methods of photonic matrix multiplication, mainly including the plane light conversion method, Mach-Zehnder interferometer method and wavelength division multiplexing method. We also summarize the developmental milestones of photonic matrix multiplication and the related applications. Then, we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years. Finally, we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration. 

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Photonic matrix multiplication lights up photonic accelerator and beyond

Optically Stimulated Luminescence Phosphors: Principles, Applications, and Prospects

We demonstrate optical networks composed of diffractive layers trained using deep learning to encode the spatial information of objects into the power spectrum of the diffracted light, which are used to classify objects with a single-pixel spectroscopic detector. Using a plasmonic nanoantenna-based detector, we experimentally validated this single-pixel machine vision framework at terahertz spectrum to optically classify the images of handwritten digits by detecting the spectral power of the diffracted light at ten distinct wavelengths, each representing one class/digit. We also coupled this diffractive network-based spectral encoding with a shallow electronic neural network, which was trained to rapidly reconstruct the images of handwritten digits based on solely the spectral power detected at these ten distinct wavelengths, demonstrating task-specific image decompression. This single-pixel machine vision framework can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with diffractive network-based spectral encoding of information.

https://advances.sciencemag.org/content/advances/7/13/eabd7690.full.pdf

Spectrally encoded single-pixel machine vision using diffractive networks

Precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics. These advances around the engineering of materials with new functionalities have also opened up exciting avenues for designing trainable surfaces that can perform computation and machine learning tasks through light-matter interaction and diffraction. Here, we analyze the information processing capacity of coherent optical networks formed by diffractive surfaces that are trained to perform an all-optical computational task between a given input and output field-of-view. We show that the dimensionality of the all-optical solution space covering the complex-valued transformations between the input and output fields-of-view is linearly proportional to the number of diffractive surfaces within the optical network, up to a limit that is dictated by the extent of the input and output fields-of-view. Deeper diffractive networks that are composed of larger numbers of trainable surfaces can cover a higher dimensional subspace of the complex-valued linear transformations between a larger input field-of-view and a larger output field-of-view, and exhibit depth advantages in terms of their statistical inference, learning and generalization capabilities for different image classification tasks, when compared with a single trainable diffractive surface. These analyses and conclusions are broadly applicable to various forms of diffractive surfaces, including e.g., plasmonic and/or dielectric-based metasurfaces and flat optics that can be used to form all-optical processors.

/pdf/all-optical-information-processing-capacity-of-diffractive-nh8a96iih0.pdf

All-Optical Information Processing Capacity of Diffractive Surfaces

Optical logic operations lie at the heart of optical computing, and they enable many applications such as ultrahigh-speed information processing. However, the reported optical logic gates rely heavily on the precise control of input light signals, including their phase difference, polarization, and intensity and the size of the incident beams. Due to the complexity and difficulty in these precise controls, the two output optical logic states may suffer from an inherent instability and a low contrast ratio of intensity. Moreover, the miniaturization of optical logic gates becomes difficult if the extra bulky apparatus for these controls is considered. As such, it is desirable to get rid of these complicated controls and to achieve full logic functionality in a compact photonic system. Such a goal remains challenging. Here, we introduce a simple yet universal design strategy, capable of using plane waves as the incident signal, to perform optical logic operations via a diffractive neural network. Physically, the incident plane wave is first spatially encoded by a specific logic operation at the input layer and further decoded through the hidden layers, namely, a compound Huygens' metasurface. That is, the judiciously designed metasurface scatters the encoded light into one of two small designated areas at the output layer, which provides the information of output logic states. Importantly, after training of the diffractive neural network, all seven basic types of optical logic operations can be realized by the same metasurface. As a conceptual illustration, three logic operations (NOT, OR, and AND) are experimentally demonstrated at microwave frequencies.

https://dr.ntu.edu.sg/bitstream/10356/138750/2/Performing%20optical%20logic%20operations%20by%20a%20diffractive%20neural%20network.pdf

Yang Sun

Papers

Performing optical logic operations by a diffractive neural network