Hany Mahrous

A multi-objective genetic algorithm is tailored to optimize the design of a wavelength interleaver/deinterleaver device. An interleaver combines data streams from two physical channels into one. The deinterleaver does the opposite job. The WDM requirements for this device include channel spacing of 50 GHz, channel bandwidth of 20 GHz, free spectral range of 100 GHz, maximum channel dispersion of 30 ps/nm, and maximum crosstalk of −23 dB. The challenges for the optimization process include the lack of a closed-form expression for the device performance and the trade-off between the conflicting performance parameters. So, for this multi-objective problem, the proposed approach maneuvers to find a compromise between the performance parameters within a few minutes, saving the designer the laborious design process previously proposed in the literature, which relies on visually inspecting the Z-plane for the dynamics of the transmission poles and zeros. Designs of better performance are achieved, with fewer ring resonators, a channel dispersion as low as 1.6 ps/nm, and crosstalk as low as −30 dB.

The advancement of silicon photonics modulators is vital for achieving high-speed optical communications. However, designing optical modulators is a complex and resource-intensive task due to the large number of design parameters. Traditionally, the design of silicon photonics modulator depends on electrical and optical simulations. This work introduces employing machine learning models as a powerful design approach for MOS-like Mach-Zehnder Modulators to overcome the traditional design complexity. The proposed design and methodology build upon prior successful efforts in developing electro-optic modulators at the device level. The RandomForestRegressor model is developed to predict the effective refractive index (n_e) and a HistGradientBoostingRegressor for the absorption coefficient (k). These models show high prediction accuracy, with a mean absolute percentage error (MAPE) of 0.02% for the effective refractive index (n_e) and 0.80% for the absorption coefficient (k) for the test dataset. The developed models can predict the performance of MOS-like Mach-Zehnder Modulators within a few milliseconds, exhibiting a minimal margin of error. These results highlight the potential of integrating machine learning in photonic device design to simplify optimization, reduce the high computational and enable efficient exploration of new design spaces.

A multi-objective genetic algorithm approach is formulated to optimize the design of silicon-photonics complex circuits with contradicting performance metrics and no closed-form expression for the circuit performance. A case study is the interleaver/deinterleaver circuit which mixes/separates optical signals into/from different physical channels while preserving the wavelength-division-multiplexing specifications. These specifications are given as channel spacing of 50 GHz, channel 3-dB bandwidth of at least 20 GHz, channel free spectral range of 100 GHz, crosstalk of −23 dB or less, and signal dispersion less than 30 ps/nm. The essence of the proposed approach lies in the formulation of the fitness functions and the selection criteria to optimize the values of the three coupling coefficients, which govern the circuit performance, in order to accommodate the contradicting performance metrics of the circuit. The proposed approach achieves the optimal design in an incomparably short period of time when contrasted with the previous tedious design method based on employing Z-transform and visual inspection of the transmission poles and zeros.

In this work, the genetic algorithm is applied to optimize the design of a silicon-photonics based interleaver/deinterleaver circuit. The interleaver circuit is used to mix two data streams into one physical channel while the deinterleaver splits them into two different channels. The circuit satisfies the wavelength division multiplexing (WDM) specifications, including a channel 3 dB-bandwidth of ±10 GHz around the maximum transmission, a channel free spectral range of 100 GHz, channel spacing of 50 GHz, crosstalk of −23 dB, and a maximum dispersion of 30 ps/nm. The circuit operates at the telecommunication wavelength of 1550 nm. The genetic algorithm is employed to achieve the best coupling coefficients between the circuit components. The developed algorithm proves very efficient as it provides the design parameters required to achieve all of the WDM requirements in a very short time compared to the previously presented design approach, which relied on excessive numerical calculations besides investigating the circuit transmission in the Z domain.

Throughout a careful step-by-step study, two simple designs of electro-optic modulators are proposed. The study shows the effect of dimensions and doping concentration on the performance parameters such as the modulation bandwidth, the size, the insertion loss and the extinction ratio. One design shows a modulation bandwidth of 54.5  GHz with a relatively short phase shifter length of 2.5  mm , a low optical insertion loss of 1.1  dB and a very high extinction ratio of 30  dB . The other one shows a bandwidth of 12.8  GHz , a phase shifter length of 9  mm , an optical insertion loss of 3.3  dB and an extinction ratio of 3  dB . It is shown that these two designs can fit to the requirements of the 5G telecommunications networks at different points which require different speeds of operation. The designs feature the complementary-metal-oxide-semiconductor technology advantages of ease and low-cost of fabrication.

Introducing high speed networks, such as the fifth generation of mobile technology and related applications including the internet of things, creates a pressing demand for hardware infrastructure that provides sufficient bandwidth. Here, silicon-based microwave-photonics presents a solution that features easy and inexpensive fabrication through a mature platform that has long served the electronics industry. In this work, the design of an electro-optic modulator is proposed where the ‘fin’ structure is adopted from the domain of electronics devices, with emphasis on the high speed of operation. The proposed modulator is customized to provide a bandwidth of 90 GHz with a small phase shifter length of 800 μm and an optical insertion loss of 4 dB. With such a speed, this proposed modulator fits high-speed applications such as modern tele-communications systems.

The following topics are dealt with: learning (artificial intelligence); image classification; feature extraction; social networking (online); medical image processing; support vector machines; data mining; pattern classification; Internet of Things; parallel processing.