In the brain, memory involves release of neurotransmitters and transport of ions through nanoconfined channels. This Perspective discusses how nanofluidic memristors emulate this confined ion transport, highlighting the materials, design strategies and challenges involved in developing brain-inspired computing technologies.
face_with_colon_three I still think that ceramics would be very useful to stop the need for global mining operations that rely heavily on rare materials when they can make the same chip from ceramics.
To be shown at ECTC 2026, May 26–29 in Orlando, USA, the new substrate technology delivers superior rigidity and circuit miniaturization for next-gen data centers, AI, and ASIC packaging.
The idea is to use glass as the substrate, or layer, on which multiple silicon chips are connected. This form of “packaging” is an increasingly popular way to build computing hardware, because it lets engineers combine specialized chips designed for specific functions into a single system. But it presents challenges, including the fact that hardworking chips can run so hot they physically warp the substrate they’re built on. This can lead to misaligned components and may reduce how efficiently the chips can be cooled, leading to damage or premature failure.
“As AI workloads surge and package sizes expand, the industry is confronting very real mechanical constraints that impact the trajectory of high-performance computing,” says Deepak Kulkarni, a senior fellow at the chip design company Advanced Micro Devices (AMD). “One of the most fundamental is warpage.”
That’s where glass comes in. It can handle the added heat better than existing substrates, and it will let engineers keep shrinking chip packages—which will make them faster and more energy efficient. It “unlocks the ability to keep scaling package footprints without hitting a mechanical wall,” says Kulkarni.
KU Leuven, Belgium bioscience engineers have developed a roadmap, so to speak, for industrial cellulose gasoline.
The bioscience engineers already knew how to make gasoline in the laboratory from plant waste such as sawdust. In 2014, at KU Leuven’s Centre for Surface Chemistry and Catalysis, the researchers succeeded in converting sawdust into building blocks for gasoline.
A chemical process made it possible to convert the cellulose – the main component of plant fibers – in the sawdust into hydrocarbon chains. These hydrocarbons can be used as an additive in gasoline. The resulting cellulose gasoline is a second-generation biofuel.
Experimental attosecond science is built around the ability to generate and control light flashes lasting billionths of a billionth of a second. Such extreme pulses can be created through high harmonic generation (HHG), where an intense laser field drives electrons out of atoms or solids and then forces them back, releasing bursts of extreme ultraviolet radiation. Techniques like this have transformed our ability to observe electron motion on its natural timescale.
To extract information from such ultrafast processes, physicists often rely on attosecond interferometry. By combining a strong laser field with a weaker second colour, different electron trajectories are made to interfere, imprinting timing and phase information onto the emitted harmonics. Over recent years, these schemes have become standard tools for attosecond metrology and spectroscopy.
It’s often the case that a dynamical system’s constituents move orders of magnitude more quickly than the collective motion that interests researchers. That disparity in scale frustrates modelers. So many computationally intensive time steps are needed to reach the final state that the computation becomes infeasible. Now Filippo Bigi of the Swiss Federal Institute of Technology in Lausanne (EPFL) and his colleagues have extended and tested an approach that uses a machine-learning model to extend the time steps in an atomic-scale simulation by an order of magnitude or more while obeying physical constraints [1]. Their method is general and could be applied to planetary systems, molecular machines, and other dynamical systems.
The EPFL researchers’ starting point was a formulation of classical mechanics that describes the evolution of a system in terms of the positions and momenta of its constituents and an energy term, the Hamiltonian. In general, these and other equations of classical mechanics satisfy fundamental geometric constraints. What’s more, approximate solutions of those equations can be made to satisfy the same constraints. Bigi and his colleagues realized that machine learning could leapfrog over many time steps while also respecting those same geometric constraints.
The researchers tested their approach on several systems, including the three-body problem of celestial dynamics and the transition of germanium telluride to a glassy state. Their simulations reproduced trusted benchmarks but with time steps ten or so times longer. Currently, enforcing the physical constraints undoes most of the computational advantage of the longer time steps. However, the team is optimistic that it can find more computationally efficient implementations.