The EU and U.S. Utilize AI to Find Alternatives to Forever Chemicals in Semiconductor Manufacturing

Category Electronics

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The EU and U.S. are collaborating to find alternative materials to PFAS, also known as forever chemicals, in semiconductor production through the use of artificial intelligence and digital twins. They also plan to review the security risk of legacy chips and increase collaboration on supply chain disruptions.

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2 minutes, 1 second

The European Union and the U.S. are joining forces to tackle the issue of so-called forever chemicals in semiconductor manufacturing, with the help of artificial intelligence. These toxic chemicals, known as PFAS, have been a source of concern due to their widespread presence in various industrial applications and their persistence in the environment. In a draft statement seen by Bloomberg, the two regions have pledged to use AI and digital twins to accelerate the search for alternative materials to PFAS in the production of semiconductors.

One of the primary goals of the joint U.S.-EU Trade and Technology Council is to enhance cooperation on emerging technologies, such as artificial intelligence.

As part of the joint U.S.-EU Trade and Technology Council, discussions are taking place in Leuven, Belgium this week, with a particular focus on emerging technologies. The use of AI in materials research is a rapidly growing trend, with the potential to greatly enhance the efficiency and speed of discovery. By leveraging AI capacities and digital twins, the EU and U.S. plan to continue identifying research opportunities to replace PFAS in semiconductor manufacturing.

PFAS chemicals have been linked to several negative health effects, including cancer, thyroid disease, and pregnancy complications.

In addition to addressing the issue of PFAS, the draft statement confirms earlier reporting by Bloomberg on the EU's plans to collaborate with the U.S. in reviewing the security risk of legacy chips in global supply chains. While these mature semiconductors play a crucial role in various industries, including automotive and aerospace, there are concerns about potential distortions in the market, as well as critical dependencies. In an effort to address this, the EU and U.S. intend to collect and share information about non-market policies and practices, and develop joint measures to mitigate any negative effects on supply chains.

The use of AI and digital twins in materials research is a growing trend in the technology industry, allowing for faster and more efficient identification of alternatives.

The U.S. and EU also plan to extend their collaboration on an early-warning mechanism and information sharing platform for supply chain disruptions for an additional three years. This will serve as a crucial tool for identifying potential vulnerabilities and addressing them in a timely manner. Additionally, a mechanism will be put in place for sharing information on public support provided to the semiconductor sector, further enhancing transparency and cooperation between the two regions.

The EU and U.S. have already invested billions of dollars in research and development for AI, indicating a commitment to utilizing the technology for the betterment of society.


The Future of AI: Solving the Energy Barrier

Category Electronics

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26 seconds

As AI technology continues to advance and tackle complex tasks, the energy barrier has emerged as a significant challenge. This barrier is caused by the plateau of Moore's law and Dennard scaling, making the traditional methods of improving computing power and efficiency no longer feasible. However, researchers are actively working on solutions, such as specialized hardware and quantum computing, to overcome this challenge and ensure the future of AI remains promising.

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The Evolution of Chip Integration: From Front-Side-Bus to On-Chip Interconnects

Category Electronics

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The integration of electronic chips in computers has evolved from front-side-bus interfaces to on-chip interconnects, enabling faster communication and improved performance. On-chip interconnects also allow for the integration of specialized processors and reduce power consumption.

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Soft, Pliable, and Wireless Optical Sensor Opens New Possibilities in Imaging Technology at Osaka University

Category Electronics

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Researchers at Osaka University have developed a flexible and wireless optical sensor using carbon nanotubes and organic transistors on an ultra-thin polymer film. This sensor is highly sensitive, works in a wide range of conditions, and can be attached to soft and curved objects, making it suitable for non-destructive analysis. Potential applications include non-destructive imaging, wearable devices, and soft robotics.

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Optical Thermometry Using Upconversion Luminescence: A Breakthrough in Temperature Measurement

Category Electronics

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Optical thermometry using upconversion luminescence offers high sensitivity and swift response times. By measuring the intensity ratio of emitted photons, temperature can be calculated. Researchers at AHUT have found promising results using Yb3+ and Ho3+ co-doped GYTO single crystal, offering potential applications in harsh conditions.

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Synaptic Transistor Mimicking Human Brain Developed By Researchers

Category Electronics

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Researchers have developed a new synaptic transistor capable of higher-level thinking that operates at room temperatures and consumes very little energy. The device is 100 times more power-efficient than conventional transistors and can store up to 100 times more information. It is ideal for real-world applications and could be used in a wide range of industries, such as AI, machine learning, IoT, robotics and wearables.

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Brain-like Computing Gains Momentum for Smaller and More Efficient Electronics

Category Electronics

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Researchers at EPFL have revealed a groundbreaking technology that seamlessly integrates two-dimensional semiconductors with ferroelectric materials to improve energy efficiency and add new functionalities in computing. The novel combination of devices combines traditional digital logic with brain-like analog operations, while the integrated Negative Capacitance Tunnel Field-Effect Transistor optimizes power consumption with a record low voltage requirement. This technology opens up a wide range of possibilities for electronic devices in the future.

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AIMC - An In-Memory Computing Solution that Promises to Revolutionize AI

Category Electronics

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IBM Research Europe recently developed an in-memory computing chip powered by phase-change memory devices. This chip was tested on deep learning datasets and was able to attain 99% accuracy while reducing energy consumption and computational times by over 50%.

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Enhancing the Light Absorption of Silicon for Low-Cost, High-Performance Photonic Devices

Category Electronics

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In response to high optical manufacturing costs, a research team from UC Davis is developing a novel approach to vastly enhance the near-infrared absorption in silicon, which could lead to affordable, high-performance photonic devices. Their findings show photon trapping led to a remarkable increase in absorption efficiency over a wide band in the NIR spectrum, exceeding that of GaAs and other group III-V semiconductors.

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Smart Digital Image Sensors to Enhance Visual Perception Capabilities

Category Electronics

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KAUST researchers have used two-dimensional materials to create a charge-trapping 'in-memory' sensor that is sensitive to visible light and can be programmed optically and erased electrically, and that can perform visual perception capabilities, such as scene recognition. Experiments indicate photo-generated charge can be trapped or stored with an extremely long-lived retention time, and the team was able to perform successful binary image recognition with an accuracy of 91%. The ultimate aim of the research is to create a single optoelectronic device that can perform optical sensing and storage with computing capabilities.

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Novel 3D printing method advances new materials discovery and production

Category Electronics

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High-throughput Combinatorial Printing (HTCP) is a groundbreaking new 3D printing method that significantly accelerates the discovery and production of new materials. The process involves mixing multiple aerosolized nanomaterial inks in a single printing nozzle, which produces materials with gradient compositions and properties and can be applied to a wide range of substances. The process enables the discovery and development of materials for a broad range of applications, from electronics to biomedical devices.

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Exploring the Potential of Programmable Neural Networks Based on Spoof Plasmonic Devices

Category Electronics

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A research team, led by Prof. Tie Jun Cui, in China has develpoed a new programmable neural network based on a so-called spoof surface plasmon polariton. This SPNN architecture can detect and process microwaves, which could be useful for wireless communication and other technological applications. SPNNs have adjustable weights and activation functions, and are able to perform an image classification task in a tenth of the time taken by conventional digital methods.

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