Graham T. Reed
University of Southampton
496 Papers
2.7K Citations
Graham T. Reed is an academic researcher from University of Southampton. The author has contributed to research in topics: Silicon photonics & Photonics. The author has an hindex of 46, co-authored 479 publications. Previous affiliations of Graham T. Reed include University of Surrey & Shanghai Jiao Tong University.
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Papers
Ion Implantation of Germanium Into Silicon for Critical Coupling Control of Racetrack Resonators
Milan Milošević,Xia Chen,Xingshi Yu,Nicholas J. Dinsdale,Ozan Aktas,Swe Zin Oo,Ali Z. Khokhar,David J. Thomson,Otto L. Muskens,Harold M. H. Chong,Anna C. Peacock,Shinichi Saito,Graham T. Reed +12 more
TL;DR: In this article, the authors report the design, fabrication and testing of silicon-on-insulator racetrack resonators, trimmed by localised annealing of germanium ion implanted silicon using continuous and pulsed wave laser sources.
A 40-Gb/s 4-V pp Differential Modulator Driver in 90-nm CMOS
TL;DR: In this article, a 40-Gb/s optical modulator driver in 90-nm CMOS technology is presented, which is based on the distributed amplifier (DA) topology with the proposed modified cascode stage to obtain high gain and large bandwidth, while also capable of protecting the MOS transistor under large output voltage swing.
Using ${\hbox {SiO}}_{2}$ Carrier Confinement in Total Internal Reflection Optical Switches to Restrict Carrier Diffusion in the Guiding Layer
TL;DR: In this paper, the authors proposed the use of a thin SiO2 barrier around the carrier injection region to improve the performance of the device and showed that high performance switching is possible by confining the carriers in this way.
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N-over-N cascode push-pull modulator driver in 130 nm CMOS enabling 20 Gbit/s optical interconnection with Mach-Zehnder modulator
TL;DR: In this paper, the N over N cascode push-pull modulator driver is demonstrated at 20Gbit/s with IBM 130nm technology, achieving 50 output impedance matching (for MZM) with on-chip termination.
Deep learning enabled design of complex transmission matrices for universal optical components
Nicholas J. Dinsdale,Peter R. Wiecha,Peter R. Wiecha,Matthew Delaney,Jamie D. Reynolds,Martin Ebert,Ioannis Zeimpekis,David J. Thomson,Graham T. Reed,Philippe Lalanne,Kevin Vynck,Otto L. Muskens +11 more
TL;DR: In this paper, a deep learning inverse network approach is proposed to design arbitrary transmission matrices using patterns of weakly scattering perturbations, allowing control over both the intensity and the phase in a multiport device at a four orders reduced device footprint compared to conventional technologies, thus, opening the door for large-scale integrated universal networks.
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