Machine learning in Electronics--

Machine learning in Electronics--

Simply put, machine learning is a subset of artificial intelligence that involves the development of computer algorithms that access large amounts of data to create models for information. These models are then used to predict specific behavior.

Technology and electronics are two fields that walk hand-in-hand, and perhaps no current development has as much likelihood to revolutionize the use of automation in businesses. The application of machine learning in electronics is limitless, yet design refinements have already shown profound results in current productions. Machine learning has the potential to push design flow past its current limitations and increase safety and efficiency.

The Basics of Machine Learning 
Perhaps it may at first seem like an overwrought method of data interpretation, but machine learning provides an avenue to unlocking automation in system processes that may otherwise be overlooked. At its core, machine learning is given some algorithm that outlines the data it finds noteworthy as well as how to parse this information from the surrounding noise. Well-trained machine learning algorithms can be used for many different kinds of data and quality assurances, such as visual inspection of component placement on a PCB or monitoring the sound waveforms of equipment to determine if there are any abnormalities compared to normal operation.

Some current and future applications of machine learning in electronics include:

VOICE COMMANDS provide a way to interact with systems at a distance. This can prevent safety issues by distancing operators from moving equipment, temperature extremes, and other occupational hazards. In addition to avoiding hazards, there can also be ergonomic gains for operators who, instead of having to stoop, bend over, or adopt other taxing postures, can indirectly access the requisite functionality.

COMPUTER VISION is the standard data format mentioned when discussing machine learning. However, the ability to visually process many small features or design elements in rapid succession, such as in a high-volume production run, would accelerate QA lead times without sacrificing accuracy.

CHIP DESIGN is a field where power, performance, and the area must all be optimized to reach the goals of future processing power in increasingly dense packages.

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