Pulsed femtosecond-laser Machining and deep reactive ion etching of diamond
Source abstract
Abstract
Abstract This paper reports the deep reactive ion etching of femtosecond laser–machined high-aspect graphitic channels through diamond. To begin, two chemical vapor deposition-grown diamond plates, one single-crystal and the other polycrystalline, are processed using the bulk microstructural modification technique of femtosecond-pulsed laser machining. Multiple laser parameters are varied in processing the polycrystalline sample, including laser pulse frequency, pulse energy, and number of pulses per micron step size between the back and the front of the diamond, whereas a constrained set of parameters is used with the single-crystal sample. Over the range of parameters evaluated for the polycrystalline diamond, Raman analysis exhibits photoluminescence from defects created near the surface through the laser machining process, but little, if any, graphite-like features. However, a Raman signal from graphite-like material is observed on the surface of the graphitic channels for the single-crystal diamond, from which it is determined that greater diamond to graphite-like phase conversion is obtained with a larger number of pulses per translation step and with a higher laser pulse energy. Further, a larger number of pulses per step results in strong luminescence, observed during Raman investigation. Subsequent deep reactive ion etching of the samples reveals that the laser–machined channels etch considerably faster near the surface than the surrounding diamond, and that a high relative etch rate is obtained even when graphite-like features are absent from the Raman spectra. The highest relative etch rate of the single-crystal sample was 1.61 μm/min.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Diamond and Carbon-based Materials Research · Laser Material Processing Techniques · Laser-induced spectroscopy and plasma
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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