Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns

Brandao, Eduardo; Nakhoul, Anthony; Duffner, Stefan; Emonet, Rémi; Garrelie, Florence; Habrard, Amaury; Jacquenet, François; Pigeon, Florent; Sebban, Marc; Colombier, Jean‐Philippe

doi:10.1103/physrevlett.130.226201

Cited by 4 publications

(8 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brandao et al strategy relied on the use of Machine Learning (ML) integrating partial physical information in the form of the SH model to identify dominating stable modes for a set of parameters "independently" from initial roughness conditions. [120] This strategy enables to solve the dual inverse problem from a single observed state, consisting of an SEM image, with little data. This modelling is scale-invariant and can be applied to any laser process.…”

Section: Nonlinear Dynamics Modellingmentioning

confidence: 99%

“…It reduces experimental irradiation parameters to simple model coefficients, which can then be optimized and extrapolated for surface pattern engineering. The generalized Swift-Hohenberg equation was derived in an adimensional form as [93,120] :…”

Section: Nonlinear Dynamics Modellingmentioning

confidence: 99%

“…This ML approach reduces experimental irradiation parameters to simple model coefficients, which can then be optimized and extrapolated for surface pattern engineering. [93,120]…”

Section: Nonlinear Dynamics Modellingmentioning

confidence: 99%

See 2 more Smart Citations

Beyond the Microscale: Advances in Surface Nanopatterning by Laser‐Driven Self‐Organization

Nakhoul,

Colombier

2024

Laser & Photonics Reviews

Self Cite

View full text Add to dashboard Cite

Designing complex local properties that seamlessly integrate efficient functions into processed materials presents a formidable challenge. A promising solution has emerged in the form of ultrafast laser‐surface structuring. Through time‐controlled polarization ultrafast irradiation at the picosecond timescale, spontaneous self‐organization of surfaces can be induced. The thermal gradient length scale unfolds on the micro‐ and nanoscale, instigating thermoconvection that leads to structured surfaces upon quenching. Convective instabilities dynamically shape intricate yet self‐regulated periodic relief structures. The ability to achieve laser‐induced self‐organization in both surface dimensions holds immense scientific importance, as it unlocks the potential to create uniform periodic 2D patterns by harnessing the inherent regulation of nonlinear dynamics processes in fluids. This comprehensive review explores recent advances in understanding and leveraging ultrafast laser‐induced self‐organization for precise patterning across versatile scales and applications. The insights herein hold the potential to drive significant advancements in nanoscale manufacturing through 2D laser‐induced periodic surface structures.

show abstract

Section: Nonlinear Dynamics Modellingmentioning

confidence: 99%

Section: Nonlinear Dynamics Modellingmentioning

confidence: 99%

See 1 more Smart Citation

Beyond the Microscale: Advances in Surface Nanopatterning by Laser‐Driven Self‐Organization

Nakhoul,

Colombier

2024

Laser & Photonics Reviews

Self Cite

View full text Add to dashboard Cite

show abstract

“…Such interest is largely boosted by the development of the laser market offering cheaper, high‐speed, and stable pulsed sources, more precise beam shaping and scanning systems as well as the growth of fundamental understanding of laser‐matter interaction via development of computer‐aided simulation tools and analysis techniques based on machine learning. [ 1–7 ]…”

Section: Introductionmentioning

confidence: 99%

Liquid‐Assisted Laser Nanotexturing of Silicon: Onset of Hydrodynamic Processes Regulated by Laser‐Induced Periodic Surface Structures

Borodaenko,

Pavlov,

Cherepakhin

et al. 2024

Adv Materials Technologies

View full text Add to dashboard Cite

Upon systematic studies of femtosecond‐laser processing of monocrystalline Si in oxidation‐preventing methanol, it is shown that the electromagnetic processes dominating at initial steps of the progressive morphology evolution define the onset of the hydrodynamic processes and morphology upon subsequent multi‐pulse exposure. Under promoted exposure quasi‐regular subwavelength laser‐induced periodic surface structures (LIPSSs) are justified to evolve through the template‐assisted development of the Rayleigh‐Plateau hydrodynamic instability in the molten ridges forming quasi‐regular patterns with a supra‐wavelength periodicity and preferential alignment along polarization direction of the incident light. Subsequent exposure promotes fusion‐assisted morphology rearrangement resulting in a spiky surface with random orientation, yet constant inter‐structure distance correlated with initial LIPSS periodicity. Along with the insight onto the physical picture driving the morphology evolution and supra‐wavelength nanostructure formation, this experiments also demonstrated that resulting quasi‐regular and random spiky morphology can be tailored by the intensity/polarization distribution of incident laser beam allowing on‐demand surface nanotexturing with diverse hierarchical surface morphologies exhibiting reduced reflectivity at visible and shortwave‐IR wavelengths. Finally, the practical attractiveness of the suggested approach for improving near‐IR photoresponse and expanding operation spectral range of vertical p‐n junction Si photodetector operating under room temperature and zero‐bias conditions via single‐step annealing‐free nanopatterning is highlighted.

show abstract

“…It is clear that these hierarchical patterns [11,12] should appear on any disordered surface of a solid material, which is affected by external stimulating factors, such as laser, electron, or ion beams [13][14][15][16][17][18][19][20][21][22][23]. The nanostructure component is crucially important since it determines the basic properties at the level of surface chemical bonds.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Structuring of Black Silicon Wafers by Ion-Flow-Stimulated Roughening Transition: Fundamentals and Applications for Photovoltaics

Gorshkov,

Stretovych,

Semeniuk

et al. 2023

Nanomaterials

View full text Add to dashboard Cite

Ion-flow-stimulated roughening transition is a phenomenon that may prove useful in the hierarchical structuring of nanostructures. In this work, we have investigated theoretically and experimentally the surface texturing of single-crystal and multi-crystalline silicon wafers irradiated using ion-beam flows. In contrast to previous studies, ions had relatively low energies, whereas flow densities were high enough to induce a quasi-liquid state in the upper silicon layers. The resulting surface modifications reduced the wafer light reflectance to values characteristic of black silicon, widely used in solar energetics. Features of nanostructures on different faces of silicon single crystals were studied numerically based on the mesoscopic Monte Carlo model. We established that the formation of nano-pyramids, ridges, and twisting dune-like structures is due to the stimulated roughening transition effect. The aforementioned variety of modified surface morphologies arises due to the fact that the effects of stimulated surface diffusion of atoms and re-deposition of free atoms on the wafer surface from the near-surface region are manifested to different degrees on different Si faces. It is these two factors that determine the selection of the allowable “trajectories” (evolution paths) of the thermodynamic system along which its Helmholtz free energy, F, decreases, concomitant with an increase in the surface area of the wafer and the corresponding changes in its internal energy, U (dU>0), and entropy, S (dS>0), so that dF=dU – TdS<0, where T is the absolute temperature. The basic theoretical concepts developed were confirmed in experimental studies, the results of which showed that our method could produce, abundantly, black silicon wafers in an environmentally friendly manner compared to traditional chemical etching.

show abstract

Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns

Cited by 4 publications

References 67 publications

Beyond the Microscale: Advances in Surface Nanopatterning by Laser‐Driven Self‐Organization

Beyond the Microscale: Advances in Surface Nanopatterning by Laser‐Driven Self‐Organization

Liquid‐Assisted Laser Nanotexturing of Silicon: Onset of Hydrodynamic Processes Regulated by Laser‐Induced Periodic Surface Structures

Hierarchical Structuring of Black Silicon Wafers by Ion-Flow-Stimulated Roughening Transition: Fundamentals and Applications for Photovoltaics

Contact Info

Product

Resources

About