University of Massachusetts researchers have successfully made a 2 nanometer individually addressable memristors. It is capable of being mass-produced in conventional fabs. This could be great for neuromorphic computing and AI in general.
This should lead to high-density memristor arrays with low power consumption for both memory and unconventional computing applications.
There were difficulties in making highly ordered and highly conductive nanoelectrode arrays. They developed “nanofins” which are metallic nanostructures with very high height-to-width ratio. This vastly reduced resistance, as the electrodes.
The memristor is a promising building block for next-generation non-volatile memory artificial neural networks and bio-inspired computing systems. Organizing small memristors into high-density crossbar arrays is critical to meet the ever-growing demands in high-capacity and low-energy consumption, but this is challenging because of difficulties in making highly ordered conductive nanoelectrodes. Carbon nanotubes, graphene nanoribbons and dopant nanowires have potential as electrodes for discrete nanodevices but unfortunately these are difficult to pack into ordered arrays. Transfer printing, on the other hand, is effective in generating dense electrode arrays but has yet to prove suitable for making fully random accessible crossbars. All the aforementioned electrodes have dramatically increased resistance at the nanoscale imposing a significant barrier to their adoption in operational circuits. Here we demonstrate memristor crossbar arrays with a 2-nm feature size and a single-layer density up to 4.5 terabits per square inch, comparable to the information density achieved using three-dimensional stacking in state-of-the-art 64-layer and multilevel 3D-NAND flash memory. Memristors in the arrays switch with tens of nanoamperes electric current with nonlinear behaviour. The densely packed crossbar arrays of individually accessible, extremely small functional memristors provide a power-efficient solution for information storage and processing.
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