Nanodevices Research Papers - Academia.edu (original) (raw)

Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations. Biology-inspired electronics is currently attracting increasing attention as modern applications of electronics, such as biomedical systems, ubiquitous sensing, or the future Internet-of-Things, require systems able to deal with significant volumes of data, with a limited power budget. In the common von Neumann architecture of computers, an order of magnitude more energy is spent accessing memory than conducting arithmetic operations. Whilst, bio-inspired computing schemes that fuse memory and computing offer significant energy savings 1. A fundamental bio-inspired architecture is the artificial neural network (ANN), a system where neurons are connected to each other through numerous synapses 2. Emerging nanoscale memories known as memristive devices have been proposed as ideal hardware analogues for the latter, while the former can be realized with standard transistor devices. Therefore, a promising way to realize neuromorphic electronics is to build a hybrid system pairing transistor " neurons " interconnected via arrays of memristive devices, each which mimics a synaptic function 3–7. Memristive nanodevices can mimic synaptic weights via non-linear conductivity, controllable by applying voltage biases above characteristic device thresholds 7,8. Simulated memristive ANNs have demonstrated capability to solve computational tasks using diverse algorithms 9–13. Few experimental demonstrations of complete memristive ANNs exist; those built so far generally exploit inorganic devices 14–19 or three terminal nanodevices 20,21. However, memristive devices can also be made with organic materials that are fundamentally attractive 22,23 as they offer unique advantages: low material costs, scalable fabrication via roll-to-roll imprint lithography, and compatibility with flexible substrates. These properties pave the way towards integration with embedded sensors, bio-medical devices, and other internet of things applications 24,25 , yet often come at the cost of slower programming relative to inorganic memristive devices or binary organic memory devices 26,27. The only ANN with organic memristors uses polyaniline polymeric devices 28 , with programming durations too slow for applications (30 s per programming pulse). Here, we introduce the first demonstrator circuit capable of learning with organically-composed memristive devices as synapses that works at speeds relevant for applications (100 μs