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Register a new userDeep-Learning-Enabled Inverse Engineering of Multi-Wavelength Invisibility-to-Superscattering Switching with Phase-Change Materials
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Inverse design of nanoparticles for desired scattering spectra and dynamic switching between the two opposite scattering anomalies, i.e. superscattering and invisibility, is important in realizing cloaking, sensing and functional devices. However, traditionally the design process is quite complicated, which involves complex structures with many choices of synthetic constituents and dispersions. Here, we demonstrate that a well-trained deep-learning neural network can handle these issues efficiently, which can not only forwardly predict scattering spectra of multilayer nanoparticles with high precision, but also inversely design the required structural and material parameters efficiently. Moreover, we show that the neural network is capable of finding out multi-wavelength invisibility-to-superscattering switching points at the desired wavelengths in multilayer nanoparticles composed of metals and phase-change materials. Our work provides a useful solution of deep learning for inverse design of nanoparticles with dynamic scattering spectra by using phase-change materials.
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Phase-change materials (PCMs) can switch between different crystalline states as a function of an external bias, offering a pronounced change of their dielectric function. In order to take full advantage of these features for active photonics and information storage, stand-alone PCMs are not sufficient, since the phase transition requires strong pump fields. Here, we explore hybrid metal-semiconductor core-shell nanoantennas loaded with PCMs, enabling a drastic switch in scattering features as the load changes its phase. Large scattering, beyond the limits of small resonant particles, is achieved by spectrally matching different Mie resonances, while scattering cancellation and cloaking is achieved with out-of-phase electric dipole oscillations in the PCM shell and Ag core. We show that tuning the PCM crystallinity we can largely vary total (~15 times) and forward (~100 times) scattering. Remarkably, a substantial reconfiguration of the scattering pattern from Kerker (zero backward) to antiKerker (almost zero forward) regimes with little change (~5%) in crystallinity is predicted, which makes this structure promising low-intensity nonlinear photonics.
We demonstrate here a controllable variation in the Casimir force. Changes in the force of up to 20% at separations of ~100 nm between Au and AgInSbTe (AIST) surfaces were achieved upon crystallization of an amorphous sample of AIST. This material is well known for its structural transformation, which produces a significant change in the optical properties and is exploited in optical data storage systems. The finding paves the way to the control of forces in nanosystems, such as micro- or nanoswitches by stimulating the phase change transition via localized heat sources.
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.
A deep learning-based wavelength controllable forward prediction and inverse design model of nanophotonic devices is proposed. Both the target time-domain and wavelength-domain information can be utilized simultaneously, which enables multiple functions, including power splitter and wavelength demultiplexer, to be implemented efficiently and flexibly.
Structural colors generated due to light scattering from static all-dielectric metasurfaces have successfully enabled high-resolution, high-saturation and wide-gamut color printing applications. Despite recent advances, most demonstrations of these structure-dependent colors lack post-fabrication tunability that hinders their applicability for front-end dynamic display technologies. Phase-change materials (PCMs), with significant contrast of their optical properties between their amorphous and crystalline states, have demonstrated promising potentials in reconfigurable nanophotonics. Herein, we leverage a tunable all-dielectric reflective metasurface made of a newly emerged class of low-loss optical PCMs with superb characteristics, i.e., antimony trisulphide (Sb$_2$S$_3$), antimony triselenide (Sb$_2$Se$_3$), and binary germanium-doped selenide (GeSe$_3$), to realize switchable, high-saturation, high-efficiency and high-resolution structural colors. Having polarization sensitive building blocks, the presented metasurface can generate two different colors when illuminated by two orthogonally polarized incident beams. Such degrees of freedom (i.e., structural state and polarization) enable a single reconfigurable metasurface with fixed geometrical parameters to generate four distinct wide-gamut colors suitable for a wide range of applications, including tunable full-color printing and displays, information encryption, and anti-counterfeiting.
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