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Multiscale multidimensional integrated imaging for precision laser processing (M2I2)

Lead Research Organisation: University of Oxford
Department Name: Engineering Science

Abstract

Precision laser processing has much potential for advanced manufacturing. Features can be machined at a fraction of a micrometre in size in a wide range of materials. The use of ultrashort laser pulses (with duration less than a picosecond) is important since all of the laser pulse energy is delivered to the focus in a timescale shorter than that for thermal diffusion. Therefore, all the material machining is done before any energy can escape as heat, which underpins the high resolution of the technique. Ultrashort laser pulses provide other unique opportunities, since they can be used for three-dimensional fabrication inside transparent materials, with a range of applications for smart technology. Such precision laser processing is already applied on an industrial scale, with examples such as accurate cutting of glass for smartphones or multi-dimensional data storage. With a constant drive for miniaturisation and enhanced functionality, the sector is destined to blossom over the next decade.

The ability to fabricate features at the sub-micrometre scale presents many opportunities for advanced technology. However, accurate positioning of such small features in three dimensions inside centimetre scale workpieces creates a serious challenge. Machine vision uses imaging solutions integrated inside the manufacturing system to provide feedback for the laser process to ensure that the device is machined as designed. However, existing hardware and software systems cannot meet the challenging demands of such high precision laser processing.

In this project, we develop new hardware and software solutions that will enable rapid three-dimensional imaging at high resolution. We also introduce new systems that can provide a macroscopic view of the entire device being processed. Additionally we establish innovative forms of optical feedback that can be applied to closely monitor the laser manufacturing process. All of this information is merged together inside a cohesive software framework, that can provide quick data transfer of important information to the laser manufacturing system. This enables quicker, more accurate laser processing of smaller features in demanding applications, to enable industrial scale manufacturing of advanced technology.

Publications

10 25 50
 
Description Working with professional software engineers, we have developed a framework which can be used to develop software modules used in hardware control that is scalable and can be shared with industrial partners.
We have shown that hyperspectral imaging during the laser fabrication process can be used to deliver critical feedback for devices in quantum technology. There is a preprint on this work which will soon be published in a high impact journal and seeded our inclusion in the IQN quantum hub.
We have applied concepts in Physics Informed Machine Learning to problems in laser manufacturing and following discussions with leaders in the field are preparing a perspective article on how ML may be used transparently and reliably in manufacturing metrology.
Working with industrial partners in the project, we have developed a protocol for live monitoring of laser machined vias in polymer samples.
Exploitation Route We expect that the various tools developed in the project will be translated to industry through licensing, collaboration or spin-out companies.
Sectors Digital/Communication/Information Technologies (including Software)

Electronics

Manufacturing

including Industrial Biotechology

 
Description The Future Advanced Metrology Hub for Sustainable Manufacturing
Amount £11,857,653 (GBP)
Funding ID EP/Z53285X/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 08/2024 
End 09/2031
 
Description Heriot Watt 
Organisation Heriot-Watt University
Country United Kingdom 
Sector Academic/University 
PI Contribution New concepts for online monitoring and feedback in laser manufacturing processes.
Collaborator Contribution Expertise in the application of laser writing to various manufacturing tasks.
Impact Novel methods for improvement of manufacturing processes.
Start Year 2023
 
Description University of Huddersfield 
Organisation University of Huddersfield
Country United Kingdom 
Sector Academic/University 
PI Contribution Development of online monitoring and feedback processes for laser based manufacturing.
Collaborator Contribution Expertise in sensing systems for online process monitoring.
Impact New methods for online monitoring of manufacturing processes.
Start Year 2023