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Researchers have experimentally demonstrated how to harness a property called negative capacitance for a new type of transistor that could reduce power consumption, validating a theory proposed in 2008 by a team at Purdue University.

The researchers used an extremely thin, or 2-D, layer of the semiconductor molybdenum disulfide to make a channel adjacent to a critical part of transistors called the gate. Then they used a “ferroelectric material” called hafnium zirconium oxide to create a key component in the newly designed gate called a negative capacitor.

Capacitance, or the storage of electrical charge, normally has a positive value. However, using the ferroelectric material in a transistor’s gate allows for negative capacitance, which could result in far lower power consumption to operate a transistor. Such an innovation could bring more efficient devices that run longer on a battery charge.

A computer chip processes and stores information using two different devices. If engineers could combine these devices into one or put them next to each other, then there would be more space on a chip, making it faster and more powerful.

Purdue University engineers have developed a way that the millions of tiny switches used to process information—called transistors—could also store that information as one device.

The method, detailed in a paper published in Nature Electronics, accomplishes this by solving another problem: combining a transistor with higher-performing memory technology than is used in most computers, called ferroelectric RAM.

Optomechanical coupling enables an on-chip frequency comb and optical time-lens for 70-fold optical pulse compression.

Alien civilizations that may use black holes as super quantum computers.

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Transistor performs energy-efficient associative learning at room temperature.

An artistic interpretation of brain-like computing. Image by Xiaodong Yan/Northwestern University.

Harvard’s breakthrough in quantum computing features a new logical quantum processor with 48 logical qubits, enabling large-scale algorithm execution on an error-corrected system. This development, led by Mikhail Lukin, represents a major advance towards practical, fault-tolerant quantum computers.

In quantum computing, a quantum bit or “qubit” is one unit of information, just like a binary bit in classical computing. For more than two decades, physicists and engineers have shown the world that quantum computing is, in principle, possible by manipulating quantum particles ­– be they atoms, ions or photons – to create physical qubits.

But successfully exploiting the weirdness of quantum mechanics for computation is more complicated than simply amassing a large-enough number of physical qubits, which are inherently unstable and prone to collapse out of their quantum states.

Quantum computing is often hailed as the next frontier of technology, promising to solve some of the most complex and challenging problems in science, engineering, and business. But how close are we to achieving this quantum dream, and what are the limitations of this emerging field?

As IEEE Spectrum shares in its detailed report, some of the leading voices in quantum computing have recently expressed doubts and concerns about the technology’s current state and prospects. They argue that quantum computers are far from being ready for practical use and that their applications are more restricted than commonly assumed.

This Review examines the development of neuromorphic hardware systems based on halide perovskites, considering how devices based on these materials can serve as synapses and neurons, and can be used in neuromorphic computing networks.

The big difference is that all the rendering would be handled by chiplets instead of a big compute chip like on its existing GPUs.