Lifeboat Foundation

Putting 50 billion transistors into a microchip the size of a fingernail is a feat that requires manufacturing methods of nanometer level precision—layering of thin films, then etching, depositing, or using photolithography to create the patterns of semiconductor, insulator, metal, and other materials that make up the tiny working devices within the chip.

The process relies heavily on solvents that carry and deposit materials in each layer—solvents that can be difficult to handle and toxic to the environment.

Now researchers led by Fiorenzo Omenetto, Frank C. Doble Professor of Engineering at Tufts, have developed a nanomanufacturing approach that uses water as the primary solvent, making it more environmentally compatible and opening the door to the development of devices that combine inorganic and biological materials.

The ferroelectric material transistor could be used to make NVMe SSDs last a whole lot longer.

TSMC making semiconductor fabs in the US has Intel Foundry grabbing as much TSMC senior engineer talent it can for its own foundry business.

A team of astrophysicists led by Caltech has managed for the first time to simulate the journey of primordial gas dating from the early universe to the stage at which it becomes swept up in a disk of material fueling a single supermassive black hole. The new computer simulation upends ideas about such disks that astronomers have held since the 1970s and paves the way for new discoveries about how black holes and galaxies grow and evolve.

“Our new simulation marks the culmination of several years of work from two large collaborations started here at Caltech,” says Phil Hopkins, the Ira S. Bowen Professor of Theoretical Astrophysics.

The first collaboration, nicknamed has focused on the larger scales in the universe, studying questions such as how galaxies form and what happens when galaxies collide. The other, dubbed STARFORGE, was designed to examine much smaller scales, including how stars form in individual clouds of gas.

Unlike classical computers, which use bits to process information as either 0s or 1s, quantum computers use quantum bits, also known as qubits, which can represent and process both 0 and 1 simultaneously thanks to a quantum property called superposition. This fundamental difference gives quantum computers the potential to solve some complex problems much more efficiently than classical computers.

INL researcher Ernesto Galvão, in collaboration with Sapienza Università di Roma (Rome) and Istituto di Fotonica e Nanotecnologie (Milan), recently published a groundbreaking study in the journal Science Advances (“Polarization-encoded photonic quantum-to-quantum Bernoulli factory based on a quantum dot source”), where they describe a new set-up for a quantum-to-quantum Bernoulli factory.

A Bernoulli factory is a method to manipulate randomness, using as inputs random coin flips with a certain probability distribution, and outputting coin flips with a different, desired distribution.

Researchers at Purdue University have trapped alkali atoms (cesium) on an integrated photonic circuit, which behaves like a transistor for photons (the smallest energy unit of light) similar to electronic transistors. These trapped atoms demonstrate the potential to build a quantum network based on cold-atom integrated nanophotonic circuits. The team, led by Chen-Lung Hung, associate professor of physics and astronomy at the Purdue University College of Science, published their discovery in the American Physical Society’s Physical Review X (“Trapped Atoms and Superradiance on an Integrated Nanophotonic Microring Circuit”).

“We developed a technique to use lasers to cool and tightly trap atoms on an integrated nanophotonic circuit, where light propagates in a small photonic ‘wire’ or, more precisely, a waveguide that is more than 200 times thinner than a human hair,” explains Hung, who is also a member of the Purdue Quantum Science and Engineering Institute. “These atoms are ‘frozen’ to negative 459.67 degrees Fahrenheit or merely 0.00002 degrees above the absolute zero temperature and are essentially standing still. At this cold temperature, the atoms can be captured by a ‘tractor beam’ aimed at the photonic waveguide and are placed over it at a distance much shorter than the wavelength of light, around 300 nanometers or roughly the size of a virus. At this distance, the atoms can very efficiently interact with photons confined in the photonic waveguide. Using state-of-the-art nanofabrication instruments in the Birck Nanotechnology Center, we pattern the photonic waveguide in a circular shape at a diameter of around 30 microns (three times smaller than a human hair) to form a so-called microring resonator. Light would circulate within the microring resonator and interact with the trapped atoms.”

A key aspect function the team demonstrates in this research is that this atom-coupled microring resonator serves like a ‘transistor’ for photons. They can use these trapped atoms to gate the flow of light through the circuit. If the atoms are in the correct state, photons can transmit through the circuit. Photons are entirely blocked if the atoms are in another state. The stronger the atoms interact with the photons, the more efficient this gate is.

Grasping the precise energy landscapes of quantum particles can significantly enhance the accuracy of computer simulations for material sciences. These simulations are instrumental in developing advanced materials for applications in physics, chemistry, and sustainable technologies. The research tackles longstanding questions from the 1980s, paving the way for breakthroughs across various scientific disciplines.

An international group of physicists, led by researchers at Trinity College Dublin, has developed new theorems in quantum mechanics that explain the “energy landscapes” of quantum particle collections. Their work resolves decades-old questions, paving the way for more accurate computer simulations of materials. This advancement could significantly aid scientists in designing materials poised to revolutionize green technologies.

The new theorems have just been published in the prominent journal Physical Review Letters. The results describe how the energy of systems of particles (such as atoms, molecules, and more exotic matter) changes when their magnetism and particle count change. Solving an open problem important to the simulation of matter using computers, this extends a series of landmark works commencing from the early 1980s.

In most neutral-atom quantum computers, atoms are held in arrays of optical tweezers. Researchers typically populate the arrays stochastically, meaning that whether a given site receives an atom is down to chance. Atoms can later be rearranged individually, but the total number of atoms depends on the success of the initial loading.

The Atom Computing team developed an iterative process to fill an array to capacity. Instead of filling the array directly, the researchers first stochastically populated a second “reservoir” array. They then transferred atoms one by one from this reservoir to the target array using an optical tweezer. Between each loading step, the researchers imaged both arrays to determine which sites in each array were occupied. This step required temporarily switching off the tweezers and holding the atoms in an optical lattice formed from interfering laser beams.

The researchers showed that this sequence could be repeated as many times as necessary without losing atoms from the target array. They also showed that they could limit atom loss during the imaging step by enhancing the lattice strength using optical cavities. This enhancement allowed the atoms to be more strongly confined without increasing the optical lattice’s laser-power requirements.

The idea of the brain as a computer is everywhere. So much so we have forgotten it is a model and not the reality. It’s a metaphor that has lead some to believe that in the future they’ll be uploaded to the digital ether and thereby achieve immortality. It’s also a metaphor that garners billions of dollars in research funding every year. Yet researchers argue that when we dig down into our grey matter our biology is anything but algorithmic. And increasingly, critics contend that the model of the brain as computer is sending scientists (and their resources) nowhere fast. Is our attraction to the idea of the brain as computer an accident of current human technology? Can we find a better metaphor that might lead to a new paradigm?