No abstract

Genetic modification to add tryptophan to PilA, the monomer for the electrically conductive pili of Geobacter sulfurreducens, yields conductive protein filaments 2000‐fold more conductive than the wild‐type pili while cutting the diameter in half to 1.5 nm.

Radiofrequency switches are critical components in wireless communication systems and consumer electronics. Emerging devices include switches based on microelectromechanical systems and phase-change materials. However, these devices suffer from disadvantages such as large physical dimensions and high actuation voltages. Here we propose and demonstrate a nanoscale radiofrequency switch based on a memristive device. The device can be programmed with a voltage as low as 0.4 V and has an ON/OFF conductance ratio up to 10 12 with long state retention. We measure the radiofrequency performance of the switch up to 110 GHz and demonstrate low insertion loss (0.3 dB at 40 GHz), high isolation (30 dB at 40 GHz), an average cutoff frequency of 35 THz and competitive linearity and power-handling capability. Our results suggest that, in addition to their application in memory and computing, memristive devices are also a leading contender for radiofrequency switch applications.

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication, and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.

Building arrays of memristive devices with sub-10 nm lateral dimensions is critical for high packing density, low power consumption, and better uniformity in device performance. Here, the authors demonstrate arrays of 8 Â 8 nm 2 cross point memristive devices using wet chemical etching and nanoimprint lithography. The devices exhibited nonvolatile bipolar switching with extreme low programming current of 600 pA. The devices also exhibited fast switching speed and improved uniformity and promising endurance and data retention. This work opens the opportunities for memristive devices in the next generation ultrahigh-density data storage and low-power high-speed unconventional computing. V