Dr. Qammer H. Abbasi Profile

Dr. Qammer H. Abbasi

at Univ of Glasgow

SPIE Involvement:

Author

Publications (5)

Proceedings Article | 24 June 2024 Poster + Paper

Dual-mode polarization-sensitive tunable metalens enabling bright-field and edge-enhanced imaging

Aqib Raza Shah, Isma Javed, Azhar Javed Satti, Qammer Abbasi, Muhammad Zubair, Muhammad Qasim Mehmood

Proceedings Volume 13020, 1302014 (2024) https://doi.org/10.1117/12.3022143

KEYWORDS: Design, Polarization, Refractive index, Lenses, Flat optics, Visible radiation, Polarized light, Metalenses, Image enhancement, Amorphous silicon

Read Abstract +

Proceedings Article | 18 June 2024 Poster + Paper

Data-driven intelligent design and optimization of chiral meta-devices

Sadia Noureen, Sumbel Ijaz, Muhammad Qasim Mehmood, Qammer Abbasi, Muhammad Zubair

Proceedings Volume 13017, 130171A (2024) https://doi.org/10.1117/12.3022048

KEYWORDS: Design, Education and training, Neural networks, Indium nitride, Mathematical optimization, Simulations, 3D modeling, Optical transmission, Reflection, Polarization

Read Abstract +

In the emerging field of photonic nano-structures, the optimization of chiral meta-surfaces has emerged as a pivotal challenge, particularly for applications such as asymmetric transmission, circular dichroism (CD) spectroscopy, imaging, and spin-selective absorption. Traditional metasurface design methodologies have often been tied to laborious parameter tuning and iterative simulations, demanding both computational resources and domain expertise. This work introduces a faster approach by leveraging advanced deep-learning algorithms to streamline the optimization of chiral meta-nano surfaces. While using diatomic unit-element as the meta-surface’s building blocks, our proposed methodology harnesses the power of neural networks to predict and refine the geometrical layout of achiral nano-bars. The proposed framework is a Tandem Inverse Model (TIM) that incorporates a forward asymmetric transmission predicting neural network (ATNN) cascaded with an inverse neural network (INN). ATNN is trained in advance to enable swift and accurate prediction of the asymmetric optical behavior of meta-atoms with an MSE as low as 5.8 × 10^-4. The complete TIM assembly is then trained together while updating the weights of INN only and keeping the pre-trained ATNN part frozen. This stacked arrangement of the forward and the inverse design models successfully addresses the fundamental non-uniqueness issue suffered in the inverse design problems. With an MSE of about 3, the trained TIM model can optimize the nano-bar’s geometrical characteristics very rapidly. The suggested model, therefore, greatly accelerates the process of designing intricate chiral meta-atoms by simultaneously optimizing eight geometrical parameters in a matter of seconds. With this model, the optimal geometrical parameters of the achiral nano-bars of the meta-atom exhibited an AT of approximately 70%. Realizing such a high AT offers several uses, including lasers, optical cloaking, and electromagnetic shielding.

Proceedings Article | 18 June 2024 Poster + Paper

Machine learning for metasurfaces broadband absorber design

Sumbel Ijaz, Anum Zulfiqar, Bacha Rehman, Qammer H. Abbasi, Muhamamd Zubair, Muhammad Qasim Mehmood

Proceedings Volume 13011, 130110H (2024) https://doi.org/10.1117/12.3022094

KEYWORDS: Design, Education and training, Principal component analysis, Performance modeling, Data modeling, Random forests, Absorption, Machine learning, Tungsten, Solar energy

Read Abstract +

Metasurfaces have been emerging increasingly due to their realization of various technologies in meeting the design of multi-functional, compact, highly efficient, tunable, and low-cost designs owing to the fact that they can manipulate electromagnetic (EM) waves in a sub-wavelength thickness. In the optical regime, they have been successful in realizing transmission, reflection and absorption for a wide range of interesting applications. The metasurface absorbers have found place in energy harvesting applications. However, their design and analysis is carried out using EM solvers which in general are heavily time-consuming due to their iterative nature of solving a problem. To mitigate the problem of slackness and computational burdensome, the machine learning (ML) is becoming popular for tackling the data related problems and have been in use for making the design of metasurfaces faster. In this work, three ML algorithms namely, XGBoost, Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) have been applied both in forward and inverse topologies for a tungsten based square-ring meta-absorber. The inverse training has been carried out by employing “principal component analysis” (PCA). The operation of a meta-absorber is dependent on its geometry; thus, the training has been carried out by varying all the geometrical features of the unit element under study. The prediction performance of the presented regression models is reckoned to be accurate that the predicted values are in the near vicinity of ground truth values. The minimum MSE for the forward model attained for the case of RF is 5.08 ×10⁻³ and that of R² is 0.9952, whereas for the inverse model, the minimum MSE of 2.05 and R² score of 0.958 with 200 PCA components is achieved. The prediction time is minimum for the LASSO algorithm which is as low as one second. The lower computation time, reliable prediction, and model-free nature of ML techniques have made them useful against data imperfections and are proven to be an effective solution to time-consuming and computationally expensive tools for metasurface design.

Proceedings Article | 17 June 2024 Poster + Paper

Revolutionizing optical wireless communication with silicon-based spin-encoded metalens

Ramna Khalid, Humberto Cabrera, Muhammad Qasim Mehmood, Qammer Abbasi, Muhammad Zubair

Proceedings Volume 13023, 130230M (2024) https://doi.org/10.1117/12.3022035

KEYWORDS: Free space optics, Silicon, Telecommunications, Wireless communications, Design, Polarization, Near infrared, Visible radiation, Signal intensity, Refractive index

Read Abstract +

Proceedings Article | 10 June 2024 Poster + Paper

Versatile broadband generation of polygonal perfect vortex beams in the visible spectrum

Muhammad Danial Shafqat, Humberto Cabrera, Qammer Abbasi, Muhammad Zubair

Proceedings Volume 12991, 129911V (2024) https://doi.org/10.1117/12.3022053

KEYWORDS: Photovoltaics, Lithium, Visible radiation, Zinc sulfide, Optical vortices, Design, Photonics, Dielectrics, Axicons, Angular momentum

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years