Machine learning optimization of Surface-Normal optical modulators for SWIR Time-of-Flight 3-D camera

Simone Bianconi, Skyler Wheaton, Min Su Park, Iman Hassani Nia, Hooman Mohseni*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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 languageEnglish (US)
Article number8201108
JournalIEEE Journal of Selected Topics in Quantum Electronics
Issue number6
StatePublished - Nov 1 2018


  • 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


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