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Design and optimization of quantum well electro-absorptive modulators using machine learning
Author(s): Simone Bianconi; Skyler Wheaton; Hooman Mohseni
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Paper Abstract

Surface-normal electro-absorptive modulators based on III-V quantum well superlattices are of interest for a large number of applications, including on-chip and free-space photonic links, detection-and-ranging and optical tagging. In recent years, novel designs of the quantum well layers stack, such as asymmetric stepped quantum well, have allowed to reach energy efficient, wide spectral bandwidth modulation performance. Nevertheless, the design of these structures is still based on intuition rather than on a quantitative assessment of the device and system performance. Moreover, the increasing number of applications has highlighted the need for a comprehensive design approach, that incorporates the performance metrics of the specific system into the design considerations.

We present a novel approach for the systematic optimization of the design of electro-absorptive modulators, based on a combination of analytical modeling and supervised machine learning. Fully-validated analytical modeling of the electronic transitions and optical propagation in the semiconductor compound is used for the training of an evolutionary algorithm, which drives the global search for optimal design.

The approach was tested for the optimization of the superlattice design of the electro-absorptive modulator for two different applications: time-of-flight 3D ranging camera, and remote sensing of electro-chemical signal via optical tagging. In both cases, a system-specific figure-of-merit is proposed and employed for the evaluation and optimization of the performance, yielding two novel optimized designs which allow for considerable performance improvement of the respective systems.

Paper Details

Date Published: 1 February 2019
PDF: 11 pages
Proc. SPIE 10947, Next-Generation Optical Communication: Components, Sub-Systems, and Systems VIII, 109470D (1 February 2019); doi: 10.1117/12.2510903
Show Author Affiliations
Simone Bianconi, Northwestern Univ. (United States)
Skyler Wheaton, Northwestern Univ. (United States)
Hooman Mohseni, Northwestern Univ. (United States)


Published in SPIE Proceedings Vol. 10947:
Next-Generation Optical Communication: Components, Sub-Systems, and Systems VIII
Guifang Li; Xiang Zhou, Editor(s)

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