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Ultra-thin MoS₂ computer packs 1,400 transistors onto one chip

The rapid advancement and diffusion of artificial intelligence (AI) systems, such as the machine learning models underpinning the functioning of ChatGPT, Gemini and similar platforms, have posed new demands on the electronics engineering industry. In fact, these systems are computationally intensive and consume substantial power, particularly when running on existing devices.

Electronics engineers worldwide have thus been trying to develop new hardware systems that can run machine learning algorithms more energy efficiently, without adversely affecting their performance. One promising approach for reducing power consumption entails the use of two-dimensional (2D) semiconductors, ultrathin materials that have already proved promising for the development of smaller electronics.

Researchers at Nanjing University, Suzhou Laboratory and Huawei Technologies Co. Ltd. recently developed and fabricated a fully functional computer based on the 2D semiconductor molybdenum disulfide (MoS₂).

Organic transistor unites memory, signal processing and light emission below 3.5 V

Seoul National University researchers have developed an ultra-low-voltage electrochemical organic light-emitting transistor that can simultaneously perform signal processing, memory and light emission within a single semiconductor device. By introducing an ion-transport enhancer into the light-emitting polymer semiconductor channel, the team enabled electric-double-layer formation at the drain electrode interface, allowing efficient electron injection without relying on the high voltages or unstable n-type doping used in conventional approaches.

As a result, the device maintained a simple single-active-layer structure while achieving both low-voltage operation and wide, spatially pinned light emission, together with neuromorphic signal-processing functionality.

The work is published in the journal Nature Materials.

Introducing Long Now Labs: Where Long-term Thinking Becomes Long-term Practice

An exciting set of opportunities for those interested in longtermism and the study of society’s flow of change through history.

An open call to participate in our inaugural lab, Protocols for the Long Now, investigating the technological forces reshaping civilizational resilience.

Neutron star merger simulations gain new precision with AI-driven r-process heating

Using a novel simulation model based on machine learning, an international research team at GSI/FAIR has succeeded in gaining a deeper understanding of element formation in stellar events such as neutron star mergers. For the first time, the scientists used deep learning with a neural network to model the energy release during r-process nucleosynthesis in hydrodynamic simulations. The results are published in the journal Physical Review D.

Many of the chemical elements we know are created in massive stellar events such as exploding stars or neutron star mergers. These events release incredible amounts of energy, allowing for the production of heavy nuclides. One key nuclear production process is the so-called rapid neutron-capture process, or r-process, in which free neutrons are captured by existing nuclei and converted into protons—thus creating larger, heavier atomic nuclei.

“Researchers around the world strive to make these complex reactions understandable through theoretical simulations. However, modeling all parameters requires incredible computing power, which is why the models often have to be simplified,” said Dr. Oliver Just, first author of the publication and a researcher in the Nuclear Astrophysics & Structure Department at GSI/FAIR. “Our new model, RHINE, which uses artificial intelligence, offers an efficient alternative.”

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