Molded interconnect devices (MID) based on laser direct structuring LPKF‐LDS® technology are widely used in the realm of electronics. Nowaday, design and development of new materials with improved properties for 3D‐MID are in focus. In this work, novel materials based on alumina ceramic with copper oxide additives are developed for this technology. The effect of the sintering temperature and the doping amount of copper oxide on the metallization quality is investigated. Furthermore, the influence of laser process parameters (e.g., laser power, velocity’, and frequency) on process output namely groove dimensions, groove profile, characteristics of structured area, and quality of the final 3D‐MID products are discussed. The novel ceramic materials show high flexibility to realize very fine structures. Moreover, the laser‐structured and metallized area shows a free edge lap with good quality. After metallization high accuracy with copper line/pitch of 16 μm/20 μm is achieved.

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The enormous applications of the molded interconnect devices (MID) make these face many difficult challenges, some of which are the quality and the reliability, as well as concerns on how to produce the micro-MID products. This drives to a hard competition between the different methods used in the MID technology. The laser direct structuring (LDS) has clearly been distinguished as the best method in comparison with the other MID processes. However, it also needs more development, especially in terms of the quality, accuracy, and their impact on the final MID quality of the two- (2D) and three-dimensional (3D) micro and normal products, especially with the decline or the limited availability of information in both of the industry and scientific research. In this work, the MID-LDS has been experimentally investigated by considering the effect of the important process parameters on the process responses and then to explore the relationship between these responses and the final MID quality and reliability. These experimental works were based on the design of the experimental tool, which employed a full factorial design for the LDS process parameters. They include the laser power, structuring speed, laser frequency, hatching percentage, the incident angle, and the focal length. The experiments have been performed on the polymer plates of Poly Ether Ether Ketone (PEEK) materials. During the experiments, different measurements have been implemented to find out the correlation between the adopted parameters and the 3D surface roughness, structuring percentage, structuring type, and ablation type, which are regarded as the most important features of LDS quality. Further measurements focus on the LDS accuracy and the final quality and reliability of the metallization structure, which includes the metallization thickness, the surface roughness, and the adhesion strength. The analysis of these experimental results showed that the quality and reliability, especially the adhesion strength and the surface roughness, of the final MID products are deeply related to the structuring percentage, structuring type, ablation type, and finally, the surface roughness.

Laser selective metallization of ceramics (LMC) has been widely applied to prepare high-quality electrical patterns on ceramic circuit carriers. As the dominating factor, surface roughness after laser treatment is controlled mostly by the laser technical parameters, such as laser power, scanning velocity, and laser frequency. Therefore, establishing an accurate relation between the surface roughness and the laser parameters will be of great benefit to the LMC specimens. In the present research, machine learning is used to simulate the LMC process on an alumina-copper oxide ceramic. The effect of each laser parameter and the interaction between them are revealed by the multiple linear regression method. The artificial neural network model trained with the Levenberg–Marquardt function provides the best estimation of the surface roughness after laser treatment compared with the Bayesian-regularization function and the scaled-conjugate-gradient function. The result can be used as a practical prediction and reasonable guideline for the optimization of LMC processes.

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