Issue link: https://iconnect007.uberflip.com/i/1505694
100 PCB007 MAGAZINE I AUGUST 2023 PCBs is extremely expensive. e tax incentive in the PCBS Act would encourage OEMs to buy American-made boards. at would cre- ate the kind of demand that would help bring more manufacturing back to the U.S. We recently had U.S. Rep. Tom Emmer (of Minnesota) visit our facility. He saw our oper- ation and heard what we need to grow and protect the industry. I was also in Washing- ton, D.C., in June at the PCBAA annual meet- ing where we met with other lawmakers to ask for their support. I joined PCBAA on the spot when Executive Director David Schild called last year, because I understood the impor- tance of having a unified voice in Washington and that we are essential in protecting national security in the United States. How can we attract young men and women to a career in microelectronics? I have found that the younger generation likes hands-on manufacturing. ey pick it up quickly. We try to keep them challenged and give them the skills to continue to grow with us. We give them a clear path to their next stage here. Our employees are our greatest asset. PCB007 Melanie Bera Anderson is president of Pro-Tech Interconnect Solutions. A team led by Nagoya University researchers in Japan has successfully predicted crystal orientation by teaching an artificial intelligence (AI) using opti- cal photographs of polycrystalline materials. The results were published in APL Machine Learning. Crystals are a vital component of many machines. Familiar materials used in industry contain polycrys- talline components, including metal alloys, ceram- ics, and semiconductors. As polycrystals are made up of many crystals, they have a complex micro- structure, and their properties vary greatly depend- ing on how the crystal grains are orientated. This is especially important for the silicon crystals used in solar cells, smartphones, and computers. "To obtain a polycrystal- line material that can be used effectively in industry, control and measurement of grain orientation distri- bution is required," Profes- sor Noritaka Usami said. A Nagoya University team consisting of Profes- sor Usami from the Gradu- ate School of Engineering and Professor Hiroaki Kudo from the Graduate School of Informatics, in collab- oration with RIKEN, have applied a machine learn- ing model that assesses photographs taken by illumi- nating the surface of a polycrystalline silicon material from various directions. They found that the AI suc- cessfully predicted the grain orientation distribution. "The time required for this measurement was about 1.5 hours for taking optical photographs, train- ing the machine learning model, and predicting the orientation, which is much faster than conventional techniques, which take about 14 hours," Usami said. Usami has high hopes for the use of the team's technique in industry. "This is a technology that will revolutionize materials development," Usami said. "This research is intended for all researchers and engineers who develop polycrystalline materi- als. It would be possible to manufacture an orien- tation analysis system of polycrystalline materials that packages an image data collection and a crys- tal orientation prediction model based on machine learning. We expect that many companies dealing with polycrystalline mate- rials would install such equipment." (Source: Nagoya Uni- versity) AI-based Technique for Predicting Crystal Orientation