TY - JOUR
T1 - Stacked Reconfigurable Optical Cavities for Smart Sensing Pixels
AU - Bilodeau, Simon
AU - De Lima, Thomas Ferreira
AU - Doris, Eli A.
AU - Hack, Michael
AU - Prucnal, Paul R.
N1 - Funding Information:
This work was supported by Universal Display Corporation
Publisher Copyright:
© 1995-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - Photonic systems have seen a recent explosion of investigation for neuromorphic engineering and the execution of machine learning models, owing to the advantages of optics for communications and performing linear operations. Here, we present a novel architecture for executing reconfigurable multiwavelength photonic multiply-accumulate (MAC) operations based on all-solid-state vertically-integrated tunable cavities. We simulate this weighting action through full transfer-matrix calculations of a realistic candidate filter stack. By efficiently leveraging the out-of-plane dimension, we form a conceptual 'smart pixel' device that can achieve higher spectral channel count per area, or equivalently computational density, than comparable subpixel architectures and planar integrated photonic devices. While this technology is of use for neuromorphic photonic information processing, its usefulness extends to applications in sensing and communications by covering, for instance, high spatial resolution hyperspectral filters and reconfigurable routing.
AB - Photonic systems have seen a recent explosion of investigation for neuromorphic engineering and the execution of machine learning models, owing to the advantages of optics for communications and performing linear operations. Here, we present a novel architecture for executing reconfigurable multiwavelength photonic multiply-accumulate (MAC) operations based on all-solid-state vertically-integrated tunable cavities. We simulate this weighting action through full transfer-matrix calculations of a realistic candidate filter stack. By efficiently leveraging the out-of-plane dimension, we form a conceptual 'smart pixel' device that can achieve higher spectral channel count per area, or equivalently computational density, than comparable subpixel architectures and planar integrated photonic devices. While this technology is of use for neuromorphic photonic information processing, its usefulness extends to applications in sensing and communications by covering, for instance, high spatial resolution hyperspectral filters and reconfigurable routing.
KW - Multiply-accumulate (MAC)
KW - neural networks
KW - neuromorphic photonics
KW - optical resonators
KW - optical sensors
KW - wavelength-divison multiplexing (WDM)
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U2 - 10.1109/JSTQE.2022.3195480
DO - 10.1109/JSTQE.2022.3195480
M3 - Article
AN - SCOPUS:85135735543
VL - 28
JO - IEEE Journal on Selected Topics in Quantum Electronics
JF - IEEE Journal on Selected Topics in Quantum Electronics
SN - 1077-260X
IS - 4
M1 - 2900212
ER -