Rapid fabrication of surface microstructures on AlN HTCC substrate by chemically assisted laser ablation
Source abstract
Abstract
Abstract Aluminium nitride high-temperature co-fired ceramic (AlN HTCC) substrate is a preferred electronic packaging material. It displays a low thermal expansion coefficient, low dielectric constant, low dielectric loss, and high thermal conductivity at a high frequency. It can also prefabricate the circuit inside. Considering the need to increase the specific surface area of AlN HTCC substrates and thereby enhance the heat dissipation performance, a rapid fabrication method involving chemically assisted laser ablation is proposed to machine microstructures on AlN HTCC substrates. The influence of power, atmosphere, and other factors on the ablation quality of AlN HTCC substrates was explored on this basis. The main component of the ablation products was Al in an Ar atmosphere. However, the modification layers in N2 and O2 atmospheres were composed of multiple components including Al, AlN, and Al2O3. The accuracy of the top and bottom widths of the microgrooves in the Ar atmosphere was higher than those for the N2 and O2 atmospheres. However, the variation of atmosphere had a negligible effect on the ablation depth. When the laser-energy density was 100 J/mm2 and the scanning distance was 10 μm, the ablation depth increased linearly from 150 μm to 350 μm as the laser power increased from 8 W to 20 W. The deviation in laser-energy density was established to predict the surface roughness and optimise the laser parameters. It was observed that the surface roughness increased with an increase in the deviation in laser-energy density. The surface roughness could be reduced by two–three times from that obtained by laser ablation with optimisation.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Metal and Thin Film Mechanics · Advanced ceramic materials synthesis · Semiconductor materials and devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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