Abstract
Surface-normal optical modulators based on multiple quantum wells are attractive for an increasing number of applications, including photonic links such as on-chip optical interconnects. The design of such structures however is still based on intuition and experience rather than on a quantitative assessment of the device and system performance, due to the extreme complexity of the device behavior and the large number of design parameters involved. We developed a method for the systematic optimization of the modulator design, using a combination of analytical modeling and supervised machine learning. The global optimization is driven by an evolutionary algorithm, and the robustness of the final results is evaluated using variance-based sensitivity analysis. The optimization algorithm was tested on the case of time-of-flight three-dimensional camera (ranging) application, yielding two novel optimized designs which allow for a considerable improvement of the depth resolution of the system. Finally, we propose a figure of merit for comparing the modulation efficiency of surface-normal modulators.
Original language | English (US) |
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Article number | 8201108 |
Journal | IEEE Journal of Selected Topics in Quantum Electronics |
Volume | 24 |
Issue number | 6 |
DOIs | |
State | Published - Nov 1 2018 |
Keywords
- 3D imaging
- Electrooptic modulation
- Infrared imaging
- integrated optoelectronics
- quantum well devices
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering