Phase Change Metamaterials for Fiber and Waveguide Integrated Compact Modulators with Built in Memory Functionality
Creators: Cui, Yihao
Publisher: name: University of Alberta Library
Year: 2023

<p>Machine learning focuses on the development of algorithms and models that enable<br>
a dynamic approach to making predictions or decisions on large data-sets which<br>
cannot be readily described by analytical models. Artificial neural networks (ANN)<br>
have become the main approach to providing advance functionalities such as pattern<br>
recognition, and inference calculations with improved performance through experience and iterative training cycles. Inspired by the structural organization of a biological brain, ANNs consist of interconnected layers of artificial neurons, or nodes,<br>
where input signals are weighted, pooled, and passed on to the next layer for analysis. Leveraging the computational hardware performance gains in application specific<br>
integrated circuits (ASICs) such as graphics processing units (GPUs), ANNs have<br>
proliferated in applications ranging from image recognition, natural language processing, autonomous vehicles, and medical diagnosis. However, the computational<br>
requirements for floating-point operations of increasingly complex ANN models have<br>
far exceeded performance trends predicted by Moore’s Law. Additionally, data movement between arithmetic and memory modules on metallic interconnects in ANN<br>
hardware accelerators does not scale in terms of energy consumption, latency, and<br>
bandwidth. The incorporation of chalcogenide phase-change metamaterials on photonic platforms such as optical fibers and photonic integrated circuits (PICs) offers a<br>
compact, reversible, and non-volatile device platform to realize a largely distributed<br>
and parallel optical computing architecture to accelerate training and inference calculations of ANNs. We demonstrate a cuboid-based germanium antimony telluride<br>
(GST) metamaterial integrated on the tip of an optical fiber with a high intensity<br>
contrast and switchable group delay dispersion between amorphous and crystalline<br>
phases for long haul telecommunication signal transmission across network nodes.<br>
Similarly, by eliciting an all-dielectric metamaterial resonance in a subwavelength<br>
structured GST grating, an optically or electrically addressable synaptic weight was<br>
realized on photonic silicon nitride waveguides. The resonator’s wavelength tunability enables symmetric 30% transmission modulation for both positive and negative<br>
weighting values across amorphous and crystalline phases. Lastly, benefiting from<br>
the high infrared transparency and the large refractive index contrast between the<br>
two phases (∆n &gt; 2.0 for certain alloy compositions), the thin film inclusion of phasechange materials on photonic waveguide circuits enables efficient and ultra-compact<br>
phase-shifter elements with memory functionalities. Furthermore, effective medium<br>
metamaterial design concepts allow for specified dispersion engineering required for<br>
low insertion loss (&lt; 0.3dB) and compact footprint (Lπ = 5 − 20µm) designs. Such<br>
non-volatile phase-shifters can be embedded within a cascaded MZI mesh to implement a programmable zero static energy consumption matrix vector multiplication<br>
layer for an interconnected neural network.</p>