AI Article Synopsis
- Real-time imaging of laser materials processing is hindered by plasma generated by the laser, which obscures direct visibility of the material.
- The plasma's structure is influenced by the sample's surface profile, allowing for indirect imaging through analysis of the plasma.
- This study showcases how deep learning can predict the surface appearance of silicon in real-time during femtosecond laser machining, providing a valuable tool for monitoring laser processing when direct observation isn't feasible.
Article Abstract
Real-time imaging of laser materials processing can be challenging as the laser generated plasma can prevent direct observation of the sample. However, the spatial structure of the generated plasma is strongly dependent on the surface profile of the sample, and therefore can be interrogated to indirectly provide an image of the sample. In this study, we demonstrate that deep learning can be used to predict the appearance of the surface of silicon before and after the laser pulse, in real-time, when being machined by single femtosecond pulses, directly from camera images of the generated plasma. This demonstration has immediate impact for real-time feedback and monitoring of laser materials processing where direct observation of the sample is not possible.
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| http://dx.doi.org/10.1364/OE.507708 | DOI Listing |
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