Result of the simulation of the light affecting the liquid geometry, which in turn affects the reflection and transmission properties of the optical mode, thus constituting a bidirectional light-liquid interaction mechanism. The degree of deformation serves as an optical memory allowing the power magnitude of the previous optical pulse to be stored and fluid dynamics to be used to affect the subsequent optical pulse in the same region of action, thus constituting an architecture where the memory is part of the process calculation Credit: Gao et al., Advanced Photonics (2022). DOI: 10.1117/1.AP.4.4.046005
Sunlight shining on water evokes the rich phenomena of liquid-light interaction, spanning spatial and temporal scales. While fluid dynamics has fascinated researchers for decades, the rise of neuromorphic computing has led to significant efforts to develop new unconventional computational schemes based on recurrent neural networks, crucial to supporting a wide range of modern technological applications. , such as pattern recognition and autonomous driving. . Since biological neurons also rely on a fluid environment, convergence can be achieved by bringing nanoscale nonlinear fluid dynamics to neuromorphic computing.
Researchers at the University of California, San Diego recently proposed a new paradigm where liquids, which normally do not interact strongly with light at the micro or nanoscale, support a significant nonlinear response to optical fields. As reported in Advanced Photonicsresearchers predict a substantial light-liquid interaction effect using a proposed nanoscale gold patch that functions as an optical heater and generates thickness changes in a liquid film covering the waveguide.
The liquid film works as an optical memory. Here’s how it works: light in the waveguide affects the geometry of the liquid surface, while changes in the shape of the liquid surface affect the properties of the optical mode in the waveguide, thus constituting mutual coupling between the optical mode and the liquid film. . Importantly, as the liquid geometry changes, the optical mode properties experience a nonlinear response; after the optical pulse stops, the magnitude of the deformation of the liquid film indicates the power of the previous optical pulse.
Nonlinear phase shift in a single waveguide with gold patch as heat source. Credit: Gao et al., Advanced Photonics (2022). DOI: 10.1117/1.AP.4.4.046005
Notably, unlike traditional computational approaches, the nonlinear response and memory reside in the same spatial region, thus suggesting the realization of a compact (beyond von-Neumann) architecture where memory and computational unit occupy the same space. The researchers show that the combination of memory and nonlinearity enables the possibility of “reservoir computing” capable of performing digital and analog tasks, such as nonlinear logic gates and handwritten image recognition.
Their model also exploits another important liquid feature: non-locality. This allows them to predict a computational improvement that is simply not possible on solid-state material platforms with a limited non-local spatial scale. Despite the non-locality, the model does not reach the levels of modern solid-state optics-based deposition computing systems, but the work presents a clear roadmap for future experimental work with the aim of validating the predicted effects and exploring complex coupling mechanisms of various physical processes. in a liquid environment for calculation.
Using multiphysics simulations to investigate the coupling between light, fluid dynamics, heat transport, and surface tension effects, the researchers predict a family of novel non-linear, non-local optical effects. They go a step further by indicating how they can be used to realize versatile and unconventional computational platforms. Taking advantage of a mature silicon photonics platform, they suggest improvements over state-of-the-art liquid-assisted computing platforms by about five orders of magnitude in space and at least two orders of magnitude in speed.
Intrinsic optical nonlinearities and carrier dynamics of InSe More information: Chengkuan Gao et al, Thin liquid film as a nonlinear optical-nolocal element and memory in the integrated optofluidic reservoir computer, Advanced Photonics (2022). DOI: 10.1117/1.AP.4.4.046005
Citation: Researchers propose neuromorphic computing with optically driven nonlinear fluid dynamics (2022, July 25) Retrieved July 25, 2022 from
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