B.M.A. Rahman

Prof. Dr. B.M.A. Rahman

orcid

Research Focus & Metrics

Academic Career Length

43Years (1984-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 October 1984 in Journal of Lightwave Technology
Verified
Journal Article
Closed access
BRB.M.A. Rahman
B.M.A. Rahman
JDJ. Davies
J. Davies
A vectorH-field finite-element method has been used for the solution of optical waveguide problems. The permittivity of the guiding structures can be an arbitrarily tensor, only limited to being lossless. To extend the domain of the field representation, infinite elements have been introduced. To eliminate spurious solutions and to improve eigenvectors, a penalty function method has been introduced. To show the validity and usefulness of this formulation, computed results are illustrated for step channel waveguide, diffused channel waveguide, anisotropic channel waveguide, and channel waveguide directional couplers.

1 January 1984 in IEEE Transactions on Microwave Theory and Techniques
Verified
Journal Article
Closed access
BRB.M.A. Rahman
B.M.A. Rahman
JDJ.B. Davies
J.B. Davies

22 August 2019 in ACS Photonics
Verified
Journal Article
Closed access
HZHanyu Zhang
Hanyu Zhang
LZLinjie Zhou
Linjie Zhou
LLLiangjun Lu
Liangjun Lu
JXJian Xu
Jian Xu
NWNingning Wang
Ningning Wang
JCJianping Chen
Jianping Chen

27 November 2019 in Optics Express
Verified
Journal Article
Closed access
PDF file
SCSunny Chugh
Sunny Chugh
AGAamir Gulistan
Aamir Gulistan
SGSouvik Ghosh
Souvik Ghosh
BRB.M.A. Rahman
B.M.A. Rahman
Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and confinement loss for a solid-core PCF. These machine learning algorithms based on artificial neural networks are able to make accurate predictions of above-mentioned optical properties for usual parameter space of wavelength ranging from 0.5-1.8 µm, pitch from 0.8-2.0 µm, diameter by pitch from 0.6-0.9 and number of rings as 4 or 5 in a silica solid-core PCF. We demonstrate the use of simple and fast-training feed-forward artificial neural networks that predicts the output for unknown device parameters faster than conventional numerical simulation techniques. Computation runtimes required with neural networks (for training and testing) and Lumerical MODE solutions are also compared.

1 August 1984 in IEEE Transactions on Microwave Theory and Techniques
Verified
Journal Article
Closed access
BRB.M.A. Rahman
B.M.A. Rahman
JDJ.B. Davies
J.B. Davies

1 June 2019 in Science Bulletin
Verified
Journal Article
Open access
HZHanyu Zhang
Hanyu Zhang
Linjie ZhouLinjie Zhou
Linjie Zhou
JXJian Xu
Jian Xu
NWNingning Wang
Ningning Wang
HHHao Hu
Hao Hu
JCJianping Chen
Jianping Chen
Low-power reconfigurable optical circuits are highly demanded to satisfy a variety of different applications. Conventional electro-optic and thermo-optic refractive index tuning methods in silicon photonics are not suitable for reconfiguration of optical circuits due to their high static power consumption and volatility. We propose and demonstrate a nonvolatile tuning method by utilizing the reversible phase change property of GST integrated on top of the silicon waveguide. The phase change is enabled by applying electrical pulses to the μm-sized GST active region in a sandwich structure. The experimental results show that the optical transmission of the silicon waveguide can be tuned by controlling the phase state of GST.