Disorder enhanced on-chip spectrometers.
Lead Research Organisation:
University of St Andrews
Department Name: Physics and Astronomy
Abstract
The ability to accurately measure the power and frequency (or wavelength) distribution of an optical signal is crucial to a vast range of applications, for spectroscopy in medicine, ensuring the safety of food or pharmaceuticals to remote sensing of gasses and fundamental science, e.g. characterising short laser pulses or finding the atmospheres of extrasolar planets. Currently, this is achieved using Optical Spectrum analyzers or optical monochromators, which have a key limitation. To achieve high-resolution they need a large optical path length and therefore large footprint (optical path length on the order of 0.5-1 m is common). Thus these devices are bulky and expensive. While not an issue for lab-based low-volume applications, this excludes their use - and thus the use of high-resolution spectroscopy - in large volume, or footprint and weight-sensitive applications, e.g. integration into lab-on-a-chip devices, mobile phones and low mass satellites (e.g. cube-sat). These applications can only be served by integrated on-chip spectrometers. Here the use of speckle spectrometers, using the random scattering of light to achieve a high wavelength resolution in an ultra-small footprint would be highly promising if it were not for the case that typical the multiple scattering needed to create the speckle results in most of the light being scattered out of the device before it can be detected. However, over the last decade, several groups (including myself) have shown that the statistical distribution of scattering sites can be used to control the amount and direction (e.g. within the plane of the device vs out-of-plane) of light scattering.
In this project we merge these advances with speckle spectrometers, i.e. using controlled disorder to efficiently generate a speckle pattern, while virtually eliminating out-of-plane scattering and optical losses. Building on this advance we will demonstrate a high resolution, low footprint on-chip spectrometer that outperforms the state of the art by orders of magnitude (in device footprint) without sacrificing the device resolution. We will also demonstrate that these devices are suitable for future large scale manufacturing, using pre-existing CMOS facilities, are suitable for gas spectroscopy and laser pulse spectrum analysis and compatible with future integration with optical detectors for a direct electronic readout.
This would present a game-changing advance in the field of integrated spectrometers and lay the foundation for future commercialization of integrated speckle spectrometers.
In this project we merge these advances with speckle spectrometers, i.e. using controlled disorder to efficiently generate a speckle pattern, while virtually eliminating out-of-plane scattering and optical losses. Building on this advance we will demonstrate a high resolution, low footprint on-chip spectrometer that outperforms the state of the art by orders of magnitude (in device footprint) without sacrificing the device resolution. We will also demonstrate that these devices are suitable for future large scale manufacturing, using pre-existing CMOS facilities, are suitable for gas spectroscopy and laser pulse spectrum analysis and compatible with future integration with optical detectors for a direct electronic readout.
This would present a game-changing advance in the field of integrated spectrometers and lay the foundation for future commercialization of integrated speckle spectrometers.
Publications
Beck P
(2023)
A high-precision silicon-on-insulator position sensor
in APL Photonics
Kumar B
(2024)
High-throughput speckle spectrometers based on multifractal scattering media
in Optical Materials Express
Kumar B
(2024)
Temperature-controlled spectral tuning of a single wavelength polymer-based solid-state random laser.
in Optics express
Kumar B
(2025)
Impact of non-Hermiticity and nonlinear interactions on disorder-induced localized modes
in APL Photonics
Schulz S
(2023)
Towards integrated position sensors with nanometer precision
Schwahn C
(2023)
Accurate and efficient prediction of photonic crystal waveguide bandstructures using neural networks
in Optics Continuum
Schwahn C.F.
(2023)
Improving Photonic Crystal Waveguide Simulation Efficiency: A Journey from 3D approaches to Deep Neural Networks
in International Conference on Metamaterials, Photonic Crystals and Plasmonics
| Description | We have found that the scatterer distribution has a strong impact on the throughput of light as anticipated and have increased the device throughput by a factor of 4, without affecting the speckle correlation, one of the key objectives of this grant. |
| Exploitation Route | This should encourage others to investigate new scatterer distributions and statistics. |
| Sectors | Aerospace Defence and Marine Chemicals Education Electronics |
| URL | https://arxiv.org/abs/2311.02796 |
| Title | Detection of scattered light using IR camera |
| Description | We have developed a new set-up that can detect the light scattered out-of-plane from an integrated photonics chip using a high-resolution IR camera purchased through this award. This method allows us to perform quantitative measurements not only in reflection and transmission but also in the out-of-plane direction, a new capability for our group. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2021 |
| Provided To Others? | No |
| Impact | This is fundamental to the completion of the rest of the project. |
| Title | Deep Neural Networks for Photonic Crystal Waveguides: Dataset |
| Description | Data set associated with the journal article "Deep Neural Networks for Photonic Crystal Waveguides" |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://research-portal.st-andrews.ac.uk/en/datasets/deep-neural-networks-for-photonic-crystal-waveg... |
| Title | High-throughput speckle spectrometers based on multifractal scattering media (dataset) |
| Description | Data set underpinning the publication "High-throughput speckle spectrometers based on multifractal scattering media" |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://research-portal.st-andrews.ac.uk/en/datasets/highthroughput-speckle-spectrometers-based-on-m... |
| Title | Shape Dependent conformable holographic metasurfaces (dataset) |
| Description | |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://risweb.st-andrews.ac.uk/portal/en/datasets/shape-dependent-conformable-holographic-metasurfa... |
| Description | Luca Dal Negro, Boston University |
| Organisation | Boston University |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Fabrication and characterisation of devices design by the partner institution |
| Collaborator Contribution | Simulation of disordered spectrometer designs |
| Impact | Multiple samples fabricated, a special issue on disordered photonics confirmed by Optical Materials Express. |
| Start Year | 2022 |
| Title | PhC simulation AI |
| Description | A pre-trained neural network that can perform the calculation of PhC structures |
| Type Of Technology | Physical Model/Kit |
| Year Produced | 2023 |
| Open Source License? | Yes |
| Impact | None to date |
| URL | https://research-portal.st-andrews.ac.uk/en/datasets/deep-neural-networks-for-photonic-crystal-waveg... |
