Source: https://www.osapublishing.org/oe/abstract.cfm?uri=oe-26-26-33678
Timestamp: 2019-04-24 20:09:54+00:00

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We present a novel procedure for manipulating the near-field of plasmonic nanoantennas using neural network-controlled laser pulse-shaping. For our model systems we numerically studied the spatial distribution of the second harmonic response of L-shaped nanoantennas illuminated by broadband laser pulses. We first show that a trained neural network can be used to predict the relative intensity of the second-harmonic hotspots of the nanoantenna for a given spectral phase and that it can be employed to deterministically switch individual hotspots on and off on sub-diffraction length scale by shaping the spectral phase of the laser pulse. We then demonstrate that a neural network trained on a 90 nm × 150 nm nano-L can, in addition, efficiently predict the hotspot intensities in an antenna with different aspect ratio, after minimal further training, for varying spectral phases. These results could lead to novel applications of machine-learning and optical control to nanoantennas and nanophotonics components.
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Fig. 1 (a) Flow diagram of the training phase of the neural network using the population generated by a genetic algorithm. A random initial population of 20 spectral phases is chosen. At each iteration, the fitness is calculated according to a pre-specified goal. The current GA population is used to incrementally train the NN (green box and arrow). The optimization procedure proceeds as follows: The fittest individuals are kept unchanged and the rest undergo mutation or cross-over. The least fit individuals are optimized by the NN using back-propagation (red box and arrow). (b) Convergence history of the GA without assistance from the NN. The graph reports the fitness of the best and worst individuals, and the average fitness. The fitness function (vertical axis) was chosen to maximize the relative SHG flux at a target hotspot of a 90 nm × 250 nm gold L-shaped nanoantenna, indicated by an arrow in the inset. (c) Similar to (b) but with the GA assisted by an un-trained NN which is incrementally trained as the GA proceeds. (d) similar to (b) but with the GA assisted by a pre-trained NN: The fitness of the best individuals converges after just one iteration, the fitness of the worst individual remains lower due to the random mutations introduced by the GA.
Fig. 2 Hotspot switching in a 90 nm × 250 nm gold nanoantenna. (a) Spectral phases which maximizes the relative flux intensity of two hotspots (labeled ’1’ and ’2’ and indicated with arrows in panel (b)) as found by the GA-NN algorithm. The spectral phases are indicated in the graph as follows: black dashed line (hotspot 1), red dashed line (hotspot 1 magnified 20 times), solid black line (hotspot 2), solid blue line (hotspot 2 magnified 20 times). The laser spectrum (orange shaded area) and the nanoantenna absorption spectrum (green shaded area) are also shown. (b)–(d) SHG flux intensities at the outer surface of the antenna for a flat phase laser pulse (b) and for the phases optimized for maximum relative flux intensity at hotspots '1' (c) and '2' (d), using the spectral phases reported in (a). Panels (e) reports similar information as panel (a) but with a different optimization goal: to maximize the absolute, rather than the relative, SH flux intensity at a specific hotspot. (f)–(g) difference between the optimized SH fluxes using the spectral phases reported in (e) and the reference SH flux obtained using a flat phase laser pulse, shown in (b).
Fig. 3 a) Relative SH flux intensity for a target hotspot of a gold L-shaped nanoantenna with size 90 nm × 250 nm for different spectral phase profiles of the incident laser pulse. The blue line refers to the simulated value, the red dashed line refers to the value predicted by the NN. The inset shows the nanoantenna with target hotspots marked. Panels (b) and (c) shows the performance of the same NN but used to predict the SHG hotspot intensities for a nanoantenna with different size: 90 nm × 150 nm with no further training, and after training only the output layer of the NN for 10 epochs. Panels (d)–(f) report similar results as panels (a)–(c) but for a different hotspot. The figure illustrates the flexibility of the NN with respect to the nanoantenna size and aspect ratio.

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