Scientists at the University of Alberta in Canada have used machine learning to perfect and automate atomic-scale manufacturing. This unprecedented advance has paved the way for large-scale production of newer electronic products that are faster, smaller, and more environmentally friendly than today's devices. . It allows smartphones to work for months with two charges, allowing computers to be hundreds of times faster, but using a thousand times less energy. Related research results were published online in the May 23 issue of the journal ACS Nano.
The next generation of circuits developed by the University's research team solves two of the biggest problems with today's electronic devices: making them energy use and resource consumption. Although modern integrated circuits have made computers faster, smaller, and cheaper over the past 30 years, they are rapidly approaching the physical limits. Some estimates predict that if people continue to maintain current energy consumption habits, the ICT industry will consume 20% of global energy by 2025, accounting for more than 5% of global carbon emissions.
This breakthrough is the result of decades of research by scientists around the world to create solutions for the advancement of atomic-scale, low-power electronics. Reducing the manufacturing process to the atomic scale produces a new type of circuit that uses less power and requires less raw materials, which is economically and environmentally beneficial.
In the past few years, scientists have made steady progress in overcoming the many obstacles brought about by working in such a small scale. In 2006, the research team created the world's sharpest object – the tip of a tungsten microscope reached an atomic width, enabling researchers to visualize and manipulate materials at the atomic scale. Three years later, they created the smallest quantum dot in history—a single silicon atom can control a single electron, paving the way for ultra-low-power circuits. Last year, the team found a way to fix atomic-level printing errors on silicon chips that would prevent ultra-small circuits from working.
The next step is to automate the production process. Researchers have successfully trained an artificial intelligence system to identify and repair precision microscopes used to fabricate atomic-scale circuits. By teaching a "neural network" system based on artificial intelligence to let it know that when an atomic microscope tip is once again sharpened into a single atom during the printing process, the research team found faster, more accurate mass production. key.
Researchers say that manufacturing in such a small range can create new functions that traditional technology can't do at all. Combining it with actual production will change the rules of the electronics industry. Atomic-scale manufacturing and mass production will become may.
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