Optics & Photonics

Harnessing Complexity: Nonlinear Optical Phenomena in L-Shapes, Nanocrescents, and Split-Ring Resonators

The Journal of Chemical Physics, Volume 161, Issue 10, 2024.

​Our recent research offers valuable insights into the design and optimization of plasmonic nanoparticles for advanced photonic applications. By elucidating the interplay between geometry, symmetry, and nonlinear optical responses, this work contributes to the development of nanoscale devices capable of efficient frequency conversion and tailored light-matter interactions.

We have systematically investigated the optical characteristics of plasmonic nanoparticles exhibiting C₂ᵥ symmetry. We focused on three distinct geometries: L-shaped particles, nanocrescents, and split-ring resonators, analyzing their linear and nonlinear optical responses.​ Utilizing the finite-difference time-domain (FDTD) method, we observed that each geometry supports two prominent plasmonic bands corresponding to their axes of symmetry. Additionally, we identified higher-frequency resonances linked to quadrupole modes and features arising from sharp structural corners.​

To delve into nonlinear phenomena, we employed a semiclassical hydrodynamic model that incorporates electron pressure effects. This approach allowed us to rigorously examine second-harmonic generation (SHG) under continuous-wave excitation. Our findings reveal that SHG spectra are highly sensitive to nanoparticle geometry and the polarization of incident light, highlighting the intricate relationship between structural design and nonlinear optical behavior.​ Furthermore, under pulsed excitation, we explored terahertz (THz) generation through difference frequency generation (DFG). The resulting THz emission spectra exhibited unique features directly tied to the plasmonic resonances and symmetrical properties of the nanoparticles. We also analyzed the polarization patterns of the emitted THz waves, uncovering complex behaviors influenced by nanoparticle geometry.​

To complement our numerical experiments, we developed an analytical framework that aligns closely with our computational results. This theoretical model provides a deeper understanding of the far-field THz intensity in relation to incident pulse parameters and the nonlinear response tensors of the nanoparticles.​

Interplay Between Gain and Loss in Arrays of Nonlinear Plasmonic Nanoparticles: Toward parametric downconversion and amplification.

Optics Letters, Volume 49, Issue 7, 2024.

​In this work, we investigated the interplay between gain and loss in arrays of nonlinear plasmonic nanoparticles, focusing on their potential for parametric downconversion and amplification. Utilizing a theoretical framework and finite-difference time-domain simulations based on the hydrodynamic-Maxwell model, we examined difference frequency generation within arrays of L-shaped metal nanoparticles characterized by intrinsic plasmonic nonlinearity. ​

Our findings reveal that the spectral characteristics near the fundamental frequency of localized surface-plasmon resonances are significantly influenced by the balance of gain and loss. By adjusting array configurations and pumping conditions, we identified specific regimes that facilitate either parametric amplification or spontaneous parametric downconversion (SPDC). Notably, our study demonstrates a strong potential for SPDC—a process crucial for generating entangled photon pairs essential in quantum computing and quantum information technologies.​

Furthermore, our metasurfaces are only a few nanometers thick, making this technology highly attractive for the compact design of photonic devices. The ultrathin nature of these metasurfaces enables seamless integration into various optical systems, paving the way for advancements in quantum optics, information processing, and nanoscale photonic devices.

ChemSpecNet: Deep Learning for Hyper-Spectral Chemical Imaging

Optics Communications, Volume 507, 15 March 2022, 127691. 

GitHub: sa-shah/ChemSpecNet

ChemSpecNet is a deep learning framework I developed to bring modern computer vision and machine learning techniques into the field of chemical imaging. It was designed to address a core limitation of Sum Frequency Generation (SFG) spectroscopic imaging: the need for spatial averaging (pixel binning) to overcome low signal-to-noise ratios, which traditionally comes at the cost of spatial resolution.

SFG imaging is a uniquely powerful method for probing surface chemistry, but its weak signals often demand long acquisition times or heavy post-processing. Conventional methods like spectral curve fitting break down in noisy environments and are computationally expensive. ChemSpecNet tackles this challenge by reimagining the problem as a spectral classification task. It uses a supervised neural network to directly identify chemical signatures from noisy pixel-level spectra, enabling high-resolution imaging without compromising detail or speed.

Trained on over a million spectra from Self-Assembled Monolayers (SAMs) on gold substrates, ChemSpecNet achieves:

Technically, ChemSpecNet is built as a fully connected neural network for Hyper-Spectral imaging using TensorFlow, with:

This project shows the power of data-driven models in domains traditionally governed by physics-based approaches. ChemSpecNet opens new possibilities for fast, high-resolution chemical imaging in materials science, nanotechnology, and biomedical sensing—setting a new standard for applying machine learning in hyperspectral and spectroscopic imaging.

QuDPy: Ultrafast Nonlinear Optical Spectroscopy Package 

Computer Physics Communications, Volume 292, November 2023, 108891 (ArXiv)

GitHub: sa-shah/QuDPy

I developed QuDPy, a Python-based tool designed to compute ultrafast nonlinear optical responses in complex systems. This open-source platform addresses the need for accurate quantum dynamical simulations, which are essential for interpreting multidimensional spectroscopic signals in fields like chemistry, biology, and physics. ​

QuDPy allows users to specify high-order optical response pathways using an intuitive input syntax that mirrors double-sided Feynman diagrams. This feature enables precise control over the time-ordering of optical interactions affecting the system's evolving density matrix. By leveraging QuTip's quantum dynamics capabilities, QuDPy can simulate spectral responses for virtually any nth-order optical process. ​

Key features of QuDPy include:

To demonstrate its utility, I provided example calculations that showcase QuDPy's ability to model various nonlinear optical phenomena. These examples serve as practical guides for researchers looking to apply the tool to their specific systems. ​

QuDPy represents a significant advancement in computational spectroscopy, offering a flexible and powerful resource for scientists exploring ultrafast nonlinear optical processes.​

Vibrational Ground-State Depletion for Enhanced Resolution Sum-Frequency Generation Microscopy

Chemical Physics Letters, Volume 787, 2022.

In this study, I introduce a novel super-resolution technique for Sum Frequency Generation (SFG) spectroscopic microscopy by applying the Ground-State Depletion (GSD) principle. Traditional SFG microscopy is limited by the diffraction of mid-IR light, restricting spatial resolution to the micron scale—too coarse for observing many nanoscale interfacial phenomena. My method overcomes this limit by shaping an IR beam into a donut profile that selectively depletes the vibrational ground state around the focal point, effectively shrinking the signal-generating region.

As a proof-of-concept, I demonstrate more than a threefold improvement in spatial resolution when imaging hydrogen-terminated silicon (Si(111):H) surfaces. This advancement allows for chemically specific imaging of surface features previously inaccessible to SFG techniques.

The work leverages vibrational dynamics, nonlinear optics, and precise beam-shaping using a custom vortex-phase-plate, showing how mid-IR light can be structured to manipulate molecular populations. My approach is scalable and adaptable to a broad range of chemical systems, paving the way for sub-micron chemical imaging without the need for near-field probes or complex nanofabrication.

Instrument development & automation for spectroscopy

Surface Spectroscopy Lab, University of Houston

Sum Frequency Generation Spectroscopy (SFG) provides an accurate and versatile tool for studying surface chemistry. At Surface Chemistry Lab, the spectroscopic system requires hardware interfacing with legacy devices, synchronous control of multiple systems, and automation. I developed the computer-hardware interface for spectroscopic components such as monochromator, time gated signal integrators, OPG/OPA (motor controls for crystals), and other instruments through communication protocols such as GPIB, Ethernet, USB, DAQ etc. Furthermore, to provide seamless automation of the spectroscopy, I developed a LabVIEW program that controls these instruments, collects the signal and performs basic spectral analysis.

The details and LabView files can be provided upon request.


SFG spectroscopy setup at Surface Lab.

Double Slit Diffraction & Quantitative Analysis with Computer Vision (R&D Optics Lab)

Supervisor: Prof. M. Sabieh Anwar (DPhil, Oxford University)

Diffraction is one of the remarkable consequences of the wave nature of light. This experiment is developed to teach the phenomenon through diffraction patterns for single slit and double slit arrangements, illustrating the relation between the shape of the diffraction pattern and that of the slit which creates it. The process has been further refined by the development and integration of computer vision and image analysis techniques. A key issue in automating quantitative analysis of diffraction patters is presented by the high dynamic range of intensities, which render standard cameras insufficient via intensity saturation. The homebuilt program captures the diffraction patters with multiple exposure levels of a standard camera to cover the entire range of intensities. It then combined these exposures to generate a HRD image by utilizing the relation between illumination and pixel intensity. Finally, a quantitative diffraction pattern is obtained by converting the pixel positions into a physical distance using geometric optics and camera calibration through computer vision. The code has been implemented in Matlab. This experiment further provides a Matlab GUI for simulating single-slit diffraction. The details can be found on LUMS University's course website here. 

Investigating Polarization of Light through Jones Calculus (R&D Optics Lab)

 Polarization is one of the fundamental properties of light. This experiment provides a fundamental understanding of the polarization as well as the effects of different optical components such as polarizing beam splitter and quarter wave plate. The analysis is performed using Jones Calculus. The details can be found on LUMS University's course website here. 

Analyzing the Polarization State of Light through the Fourier Series (R&D Optics Lab)

One can completely determine the polarization of light by simply using a polarizer and a quarter wave plate. This optical setup provides a method to generate as well as analyze different polarization of light using these two components and Fourier transform. The details can be found on LUMS University's course website here. 

Temperature modulation of diode laser wavelength (R&D Optics Lab)

The output power and wavelength of a laser diode can be modulated by varying its current and temperature. This experiment has been developed to provide an understanding of how a laser diode’s optical power and wavelength can be varied by controlling its temperature and operating current. The temperature control has been implemented through integrating Peltier heaters in laser diode mount TCLDM09 with a homebuilt power circuit controlled by a homebuilt LabView program that operates on a Partial Integral Control logic. The results indicate that the wavelength can be varied from 784nm to 797 nm with a temperature variation of 20 to 55 degree C. The details can be found on LUMS University's course website here.