Related papers: Second-harmonic generation for enhancing the perfo…
The ever-increasing demand for higher data rates in communication systems intensifies the need for advanced non-linear equalizers capable of higher performance. Recently artificial neural networks (ANNs) were introduced as a viable…
Using symmetry arguments we show how optical second harmonic generation (SHG) can be used to detect antiferromagnetism at surfaces and in thin films. Based on the group theoretical analysis of the nonlinear electric susceptibility we…
Second harmonic generation (SHG) is a fundamental nonlinear optical phenomenon widely used both for experimental probes of materials and for application to optical devices. Even-order nonlinear optical responses including SHG generally…
Integrating 2D materials onto on-chip photonic devices holds significant potential for nonlinear frequency conversion across various applications. The lack of inversion symmetry in monolayers of transition metal dichalcogenides (TMDs),…
Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN). Unfortunately, the non-linear operations and the high-precision requirements of DNNs…
Integrating hyperspectral imagery (HSI) with deep neural networks (DNNs) can strengthen the accuracy of intelligent vision systems by combining spectral and spatial information, which is useful for tasks like semantic segmentation in…
Deep neural networks (DNNs) have become an enabling component for a myriad of artificial intelligence applications. DNNs have shown sometimes superior performance, even compared to humans, in cases such as self-driving, health applications,…
Recently, Vision Graph Neural Network (ViG) has gained considerable attention in computer vision. Despite its groundbreaking innovation, Vision Graph Neural Network encounters key issues including the quadratic computational complexity…
The PhD thesis is devoted to the study of second harmonic generation of narrow beams in photonic crystals. The basic idea is that if both frequencies, the fundamental and second harmonics are in the region of self-collimation, then the…
The ubiquity of neural networks (NNs) in real-world applications, from healthcare to natural language processing, underscores their immense utility in capturing complex relationships within high-dimensional data. However, NNs come with…
In recent years, the widespread use of deep neural networks (DNNs) has facilitated great improvements in performance for computer vision tasks like image classification and object recognition. In most realistic computer vision applications,…
Second harmonic generation microscopy (SHG) is a powerful imaging modality which has found applications in investigating both biological and synthetic nanostructures. Like all optical microscopy techniques, the resolution of SHG is limited…
The emerging field of silicon photonics seeks to unify the high bandwidth of optical communications with CMOS microelectronic circuits. Many components have been demonstrated for on-chip optical communications, including those that utilize…
We study the effective second-harmonic generation (SHG) susceptibility in graded metallic films by invoking the local field effects exactly, and further numerically demonstrate that the graded metallic films can serve as a novel optical…
Artificial neural networks (ANNs) have now been widely used for industry applications and also played more important roles in fundamental researches. Although most ANN hardware systems are electronically based, optical implementation is…
We present a combined experimental and theoretical study of enhanced third-harmonic generation (THG) in silicon nitride metasurfaces. These structures exhibit strong resonant nonlinear responses, enabling up to two orders of magnitude…
High harmonic generation (HHG) is a nonlinear process that enables table-top generation of tunable, high-energy, coherent, ultrashort radiation pulses in the extreme ultraviolet (EUV) to soft X-ray range. These pulses find applications in…
Deep neural networks (DNNs) transform stimuli across multiple processing stages to produce representations that can be used to solve complex tasks, such as object recognition in images. However, a full understanding of how they achieve this…
Deep Neural Networks (DNNs) are increasingly deployed across applications. However, ensuring their reliability remains a challenge, and in many situations, alternative models with similar functionality and accuracy are available.…
Plasmonic resonances in metallic nanostructures have been shown to drastically enhance local electromagnetic fields, and thereby increase the efficiency of nonlinear optical phenomena, such as second harmonic generation (SHG). While it has…