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10/07/2025 | Dr. Jennie Hwang -- Column: SMT Perspectives and Prospects
Data is one of the six pillars of AI infrastructure. It is critical to the performance of artificial intelligence (AI) models. AI data, essential to both the training and inference of Generative AI models, connotes the datasets used to train, validate, and test AI models. Training data provides models with a frame of reference by establishing a baseline against which models can compare new data using pre-trained models for predictions or generating new content.

U.S. Uses Secret Trackers to Trace AI Chips Diverted to China, Sources Say

08/18/2025 | I-Connect007 Editorial Team
Two sources told Reuters that U.S. authorities have secretly placed location trackers in some advanced chip shipments they see as at high risk of illegal diversion to China. They said the trackers are intended to locate AI chips that are sent to locations restricted by U.S. export laws, but authorities only examine some shipments.

Happy’s Tech Talk #38: Novel Metallization for UHDI

05/07/2025 | Happy Holden -- Column: Happy’s Tech Talk
I have been involved in high-density electronics substrates since 1970 when I joined Hewlett-Packard’s RF semiconductor group after college. Figure 1 shows the difference between trace/space lithography for substrates and silicon starting in 1970. My projects involved sapphire circuits for RF devices, but the figure displays the state of PCBs and integrated CMOS circuits and their packaging, not discreet RF devices. Even then, semiconductors were 50X higher density.

New Database of Materials Accelerates Electronics Innovation

05/05/2025 | ACN Newswire
In a collaboration between Murata Manufacturing Co., Ltd., and the National Institute for Materials Science (NIMS), researchers have built a comprehensive new database of dielectric material properties curated from thousands of scientific papers.

New Database of Materials Accelerates Electronics Innovation

05/02/2025 | ACN Newswire
In a collaboration between Murata Manufacturing Co., Ltd., and the National Institute for Materials Science (NIMS), researchers have built a comprehensive new database of dielectric material properties curated from thousands of scientific papers.
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