Laser micro machining beyond the diffraction limit using a photonic nanojet

Uenohara, Tsutomu; Takaya, Yasuhiro; Mizutani, Yasuhiro

doi:10.1016/j.cirp.2017.04.068

Cited by 18 publications

(5 citation statements)

References 18 publications

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“…It is the authors' view that the implementation of such a technique would enable significant improvements to quality control in laser machining including the automation and control of tasks where process conditions are not known before machining commences [197]-for example, the laser cleaning of rust. Such process control may be of particular importance for the future of high-precision ultrafast laser machining, where extremely high non-linearities provide the opportunity for techniques such as sub-diffraction-limit fabrication [53,191,224,225] but with associated extreme sensitivity to fluctuations in the laser parameters (which could therefore be compensated in real time via machine learning).…”

Section: Real-time Error Compensationmentioning

confidence: 99%

Lasers that learn: The interface of laser machining and machine learning

Mills

Grant-Jacob

2021

IET Optoelectronics

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Laser machining is a highly flexible non-contact fabrication method used extensively across academia and industry. Whilst simulations based on fundamental understanding offer some insight into the processes, both the highly non-linear interactions between laser light and matter and the variety of materials involved mean that theoretical modelling is not particularly applicable to practical experimentation. However, recent breakthroughs in machine learning have resulted in neural networks that are capable of accurate and rapid modelling of laser machining at a scale, speed, and precision well beyond those of existing theoretical approaches with applications including 3-D surface visualisation and real-time error correction. A perspective at the intersection of laser machining and machine learning is presented, followed by a discussion of future milestones and challenges for this field.10 steps each, that corresponds to a total of 10^5 experimental trials. Because of the often highly non-linear relationships amongst parameters, the standard practice is generally the systematic collection of laser-machining data for all parameter combinations to identify the optimal combination. However, this process is both time-consuming and unfocussed and can take days or weeks, hence costing unnecessary effort, time, and money. Even when the optimal parameters have been determined, small changes during manufacturing, for example in laser power or beam shape, can result in final product quality that is below the required standard, again with associated costs in time and money. What is needed, therefore, is a set of modelling methodologies for identifying optimal parameters and providing real-time monitoring and error correction during manufacturing.However, the processes that describe laser machining, such as the light-matter interaction, heat conduction, phase change of material, and material removal, are particularly complex and hence challenging to model precisely and directly from a theoretical standpoint. The exact details of these processes depend on many factors, including laser parameters (e.g. wavelength, fluence, and pulse length), associated diffraction effects, and sample properties (e.g. absorption, reflectivity, melting temperature, and ablation threshold). In addition, different interaction effects become dominant as the temporal interaction length varies from continuous wave to ultrafast (i.e. femtosecondThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Real-time Error Compensationmentioning

confidence: 99%

Lasers that learn: The interface of laser machining and machine learning

Mills

Grant-Jacob

2021

IET Optoelectronics

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“…At the same time, the PH parameters formed in water can be optimized by changing the contrast of the refractive index of the fiber material and the medium. Moreover, the results of this work may be of interest for surface texturing [ 35–37 ] and nanostructuring, biosensing, [ 38 ] fiber‐based subwavelength optical micromanipulation, [ 39–41 ] improvement of data storage, engraving and microwelding, to name a few. These subjects will be investigated in the further works.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the results of this work may be of interest for surface texturing [31][32][33] and nanostructuring, biosensing [34], fiber-based sub-wavelength optical micromanipulation [35][36][37], improvement of data storage, engraving, micro-welding, to name a few. This will be investigated.…”

Section: Discussionmentioning

confidence: 98%

Concept of photonic hook scalpel generated by shaped fiber tip with asymmetric radiation

Minin

Liu²,

Tuchin

et al. 2020

Journal of Biophotonics

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Structured light have made deep impacts on modern biotechnology and clinical practice, with numerous optical systems and lasers currently being used in medicine to treat disease. We demonstrate a new concept of fiber‐based optical hook scalpel. The subwavelength photonic hook is obtained in the vicinity of a shaped fiber tip with asymmetric radiation. A 1550 nm continuous‐wave source, commonly used for medical imaging, has been required. Photonic hook with a lateral feature size less than the half‐wavelength is achieved using a hemispherical shaped fiber tip with metallic mask. This breakthrough is carried out in ambient air by using a 4‐μm‐diameter fiber with a shaped tip. A good correlation is observed between the computed intensity distribution of photonic hook and the tip sizes. Photonic hook generated with a shaped fiber tip, easier to manipulate, shows far‐reaching benefits for potential applications such as ophthalmic laser surgery, super‐resolution microscopy, photolithography and material processing.

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“…Few theoretical studies related to the PNJs of single dielectric microspheres under pulsed irradiation (or coherent broadband illumination) are also reported in the literature 60 , 61 . Under the pulsed irradiation, the PNJs appear to be non-stationary and these are found useful in micromachining beyond the diffraction limit 62 , microscopy 63 , and non-linear optics 64 . However, details of the characteristic parameters of the PNJs generated by focused polychromatic light (or broadband radiation) and the role of R s , n m, and ω 0 on the characteristic parameters of the PNJs generated by broadband radiation are not reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Characteristic parameters of photonic nanojets of single dielectric microspheres illuminated by focused broadband radiation

Mandal

Tiwari

Dantham

2022

Sci Rep

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Herein, we report the theoretical investigation on the photonic nanojets (PNJs) of single dielectric microspheres illuminated by focused broadband radiation (polychromatic light) from a Halogen lamp, supercontinuum source, light-emitting diode, and Hg arc lamp. The role of incident beam waist, refractive index of the surrounding medium, and radius of the microsphere on the characteristic parameters such as the electric field intensity enhancement, effective width, and length of the PNJ is studied. Interestingly, the characteristic parameters of the PNJs of solid microspheres obtained for the above-mentioned broadband radiation sources are found close to those observed for the focused monochromatic radiation of wavelengths which are near to the central wavelengths of the sources. Moreover, the characteristic parameters of PNJs of the core–shell microspheres of different thicknesses (t) illuminated by polychromatic radiation from most commonly used sources such as Halogen and Hg arc lamps are studied. For each t value, a suitable wavelength of monochromatic radiation has been found to generate the PNJ with characteristic parameters which are close to those obtained in the case of polychromatic radiation. We believe that the analytical theory and the theoretical simulations reported here would be useful for researchers who work in the fields such as PNJ assisted photoacoustic spectroscopy, white light nanoscopy, low-coherence phase-shifting interference microscopy, and Mirau interferometry.

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Laser micro machining beyond the diffraction limit using a photonic nanojet

Cited by 18 publications

References 18 publications

Lasers that learn: The interface of laser machining and machine learning

Lasers that learn: The interface of laser machining and machine learning

Concept of photonic hook scalpel generated by shaped fiber tip with asymmetric radiation

Characteristic parameters of photonic nanojets of single dielectric microspheres illuminated by focused broadband radiation

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