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Design007-July2022

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JULY 2022 I DESIGN007 MAGAZINE 65 tains 75% bio-organic content from renew- able sources. New developments are in the pipeline and, with time, these UV cure, sol- vent-free, and bio-based materials will open up new avenues for compliance across many disciplines. DESIGN007 Saskia Hogan is global product manager, conformal coatings, at Electrolube. To read past columns from Electrolube, click here. Download your free copy of Electrolube's book, The Printed Circuit Assembler's Guide to… Conformal Coatings for Harsh Environ- ments, and watch the micro webinar series "Coatings Uncoated!" UV/chemical cure remained virtually crack free aer 1,000 thermal shock cycles (-40°C to +130°C) and also completed curing within six hours compared to the extremely long time required to complete the UV/moisture cure. e UV/chemical-cure material had far greater stability of the key physical properties during thermal aging when compared to UV/ moisture-cure materials. is increased sta- bility led to a considerably improved thermal shock performance when compared to the tra- ditional UV/moisture-cure materials. So, there you have it: Environmentally friendly materials are available now, including our unique bio-based UVCLX coating, which guarantees full cure within 24 hours and con- by Jorge Gonzalez and Luke Roberto CADENCE Even though we hear the terms artificial intelli- gence (AI) and machine learning (ML) almost daily, there's still a lot of confusion about the actual mean- ing of these designations. In a nutshell, AI is an umbrella term embracing technologies that empower machines to simulate human behavior. ML is a sub- set of AI that allows machines to automatically learn from past data and events without explicitly being programmed to do so. Another perception is that AI is a relatively new development, whereas, in reality, scientists and engi- neers have been working on AI-related technologies for decades. In fact, the founding event that led to AI as we know it today was a Dartmouth workshop— the Dartmouth Summer Research Project on Artifi- cial Intelligence—in 1956. We can divide the development of AI (and ML) into two eras. During the first era, until circa 2012, AI computational requirements doubled approximately every two years, roughly tracking Moore's Law. How- ever, around 2012, developments in AI architectures and algorithms led to an inflection point that marked the beginning of the second (modern) era of AI, in which compute requirements for enterprise-level AI systems started to double approximately every three-and-a-half months (Figure 1). Fortunately, this demand for computational power can be satisfied using the tremendous XPU (CPU, GPU, FPGA, etc.) and memory resources made available by modern cloud computing environments. Today, AI and ML technologies are becom- ing ubiquitous, from handwriting recognition applications on tablet computers to natural language speech recognition and genera- tion in smart appliances, machine vision with object detection and recognition capabili- ties in robots, predictive maintenance appli- cations in the automotive industry—the list goes on. To read this entire column, click here. All Systems Go Accelerate Your PCB Designs with Machine Learning

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