Multi‐level Electro‐thermal Switching of Optical Phase‐Change Materials Using Graphene
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adpr.202000034.pdf
Description
Published version
Size
1.95 MB
Format
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Checksum (MD5)
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Author(s)
Ríos, Carlos
Zhang, Yifei
Shalaginov, Mikhail Y
Deckoff-Jones, Skylar
Wang, Haozhe
An, Sensong
Zhang, Hualiang
Kang, Myungkoo
Richardson, Kathleen A
Roberts, Christopher
Date Issued
2021
Journal
Advanced Photonics Research
Publisher
Wiley
Citation
Ríos, Carlos, Zhang, Yifei, Shalaginov, Mikhail Y, Deckoff-Jones, Skylar, Wang, Haozhe et al. 2021. "Multi‐level Electro‐thermal Switching of Optical Phase‐Change Materials Using Graphene." Advanced Photonics Research, 2 (1).
Version
Final published version
Abstract
Reconfigurable photonic systems featuring minimal power consumption are
crucial for integrated optical devices in real-world technology. Current active
devices available in foundries, however, use volatile methods to modulate
light, requiring a constant supply of power and significant form factors.
Essential aspects to overcoming these issues are the development of nonvolatile
optical reconfiguration techniques which are compatible with on-chip
integration with different photonic platforms and do not disrupt their optical
performances. In this paper, a solution is demonstrated using an optoelectronic
framework for nonvolatile tunable photonics that employs undoped-graphene
microheaters to thermally and reversibly switch the optical phase-change
material Ge$_2$Sb$_2$Se$_4$Te$_1$ (GSST). An in-situ Raman spectroscopy method
is utilized to demonstrate, in real-time, reversible switching between four
different levels of crystallinity. Moreover, a 3D computational model is
developed to precisely interpret the switching characteristics, and to quantify
the impact of current saturation on power dissipation, thermal diffusion, and
switching speed. This model is used to inform the design of nonvolatile active
photonic devices; namely, broadband Si$_3$N$_4$ integrated photonic circuits
with small form-factor modulators and reconfigurable metasurfaces displaying
2$\pi$ phase coverage through neural-network-designed GSST meta-atoms. This
framework will enable scalable, low-loss nonvolatile applications across a
diverse range of photonics platforms.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Lincoln Laboratory
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1002/ADPR.202000034
