The ability to form integrated circuits on flexible sheets of plastic enables attributes (for example conformal and flexible formats and lightweight and shock resistant construction) in electronic devices that are difficult or impossible to achieve with technologies that use semiconductor wafers or glass plates as substrates. Organic small-molecule and polymer-based materials represent the most widely explored types of semiconductors for such flexible circuitry. Although these materials and those that use films or nanostructures of inorganics have promise for certain applications, existing demonstrations of them in circuits on plastic indicate modest performance characteristics that might restrict the application possibilities. Here we report implementations of a comparatively high-performance carbon-based semiconductor consisting of sub-monolayer, random networks of single-walled carbon nanotubes to yield small- to medium-scale integrated digital circuits, composed of up to nearly 100 transistors on plastic substrates. Transistors in these integrated circuits have excellent properties: mobilities as high as 80 cm(2) V(-1) s(-1), subthreshold slopes as low as 140 m V dec(-1), operating voltages less than 5 V together with deterministic control over the threshold voltages, on/off ratios as high as 10(5), switching speeds in the kilohertz range even for coarse (approximately 100-microm) device geometries, and good mechanical flexibility-all with levels of uniformity and reproducibility that enable high-yield fabrication of integrated circuits. Theoretical calculations, in contexts ranging from heterogeneous percolative transport through the networks to compact models for the transistors to circuit level simulations, provide quantitative and predictive understanding of these systems. Taken together, these results suggest that sub-monolayer films of single-walled carbon nanotubes are attractive materials for flexible integrated circuits, with many potential areas of application in consumer and other areas of electronics.

High leakage current in deep-

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Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. However, their application in machine learning have largely been limited to very shallow neural network architectures for simple problems. In this paper, we propose a novel algorithmic technique for generating an SNN with a deep architecture, and demonstrate its effectiveness on complex visual recognition problems such as CIFAR-10 and ImageNet. Our technique applies to both VGG and Residual network architectures, with significantly better accuracy than the state-of-the-art. Finally, we present analysis of the sparse event-driven computations to demonstrate reduced hardware overhead when operating in the spiking domain.

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