AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks

In this paper, we present AnalogVNN, a simulation framework built on PyTorch which can simulate the effects of optoelectronic noise, limited precision, and signal normalization present in photonic neural network accelerators. We use this framework to train and optimize linear and convolutional neural networks with up to 9 layers and ~1.7 million parameters, while gaining insights into how normalization, activation function, reduced precision, and noise influence accuracy in analog photonic neural networks. By following the same layer structure design present in PyTorch, the AnalogVNN framework allows users to convert most digital neural network models to their analog counterparts with just a few lines of code, taking full advantage of the open-source optimization, deep learning, and GPU acceleration libraries available through PyTorch

Supplementary document for DESIGNING FAST AND EFFICIENT ELECTRICALLY-DRIVEN PHASE CHANGE PHOTONICS THROUGH A MULTIPHYSICS SIMULATION FRAMEWORK - 5724084.pdf

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Layer-Tunable Third-Harmonic Generation in Multilayer Black Phosphorus

Black phosphorus has been the subject of growing interest due to its unique band structure that is both layer dependent and anisotropic. While many have studied the linear optical response of black phosphorus, the nonlinear response has remained relatively unexplored. Here we report on the observation of third-harmonic generation in black phosphorus using an ultrafast near-IR laser and measure χ⁽³⁾ experimentally for the first time. It was found that the third-harmonic emission is highly anisotropic, dependent on the incident polarization, and varies strongly with the number of layers present due to signal depletion and phase-matching conditions

Realization of an integrated photonic platform for coherent photo-electric processing

In this paper, we demonstrate an integrated, coherent approach to processing temporally multiplexed optical signals using a modular dot-product unit cell. We use these unit cells to demonstrate multiply accumulate operations on real- and complex-valued inputs using coherent detection and temporal integration. We then extend this to computing the covariance between stochastic bit streams which can be used to estimate correlation between data streams in the optical domain. Finally, we demonstrate a path to scaling up our platform to enable general matrix-matrix operations. Our approach has the potential to enable highly efficient and scalable optical computing on-chip for a broad variety of AI applications

Multifunctional Graphene Optical Modulator and Photodetector Integrated on Silicon Waveguides

Graphene’s unique optoelectronic properties have been exploited for many photonic applications. Here, we demonstrate a single graphene-based device that simultaneously provides efficient optical modulation and photodetection. The graphene device is integrated on a silicon waveguide and is tunable with a graphene gate to achieve a near-infrared photodetection responsivity of 57 mA/W and modulation depth of 64% with GHz bandwidth. Simultaneous modulation of photocurrent and optical transmission has been achieved, which may lead to unprecedented optoelectronic applications

Non-volatile silicon photonic memory with more than 4-bit per cell capability

We present the first demonstration of an integrated photonic phase-change memory using GeSbTe-225 on silicon-on-insulator and demonstrate reliable multilevel operation with a single programming pulse. We also compare our results on silicon with previous demonstrations on silicon nitride. Crucially, achieving this on silicon enables tighter integration of traditional electronics with photonic memories in future, making phase-change photonic memory a viable and integrable technology

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Strong Opto-Structural Coupling in Low Dimensional GeSe₃ Films

Chalcogenide glasses as nanoscale thin films have become leading candidates for several optical and photonic technologies, ranging from reflective displays and filters to photonic memories. Current material systems, however, show strong optical absorption which limits their performance efficiencies and complicates device level integration. Herein, we report sputter deposited thin films of GeSe3, which are low loss and in which the flexible nature of the atomic structure results in thermally activated tunability in the refractive index as well as in the film’s physical volume. Such changes, which occur beyond a threshold temperature are observed to be accumulative and directed toward a more equilibrium amorphous state of the film, instead of crystallization. Our results provide insight into a new type of configurability that is based on strong coupling in the material’s opto-structural properties. The low optical losses in this material system combined with the tunability in the optical properties in the visible and near-infrared have direct application in higher performing optical coatings and in corrective optics

Engineering photonic environments for two-dimensional materials

A fascinating photonic platform with a small device scale, fast operating speed, as well as low energy consumption is two-dimensional (2D) materials, thanks to their in-plane crystalline structures and out-of-plane quantum confinement. The key to further advancement in this research field is the ability to modify the optical properties of the 2D materials. The modifications typically come from the materials themselves, for example, altering their chemical compositions. This article reviews a comparably less explored but promising means, through engineering the photonic surroundings. Rather than modifying materials themselves, this means manipulates the dielectric and metallic environments, both uniform and nanostructured, that directly interact with the materials. For 2D materials that are only one or a few atoms thick, the interaction with the environment can be remarkably efficient. This review summarizes the three degrees of freedom of this interaction: weak coupling, strong coupling, and multi-functionality. Also, it reviews a relatively timing concept of engineering that directly applied to the 2D materials by patterning. Benefiting from the burgeoning development of nanophotonics, the engineering of photonic environments provides a versatile and creative methodology of reshaping light-matter interaction in 2D materials