The Role of the Internal Capacitance in Organic Memristive Device for Neuromorphic and Sensing Applications

Organic electronics has recently emerged as a promising candidate for the emulation of brain-like functionalities, especially at the device level. Among the proposed technologies, memristive devices have gained an increasing attention due to their non-volatile behavior which makes them suitable for the implementation of artificial neuronal networks. However, most of them have an energy-costly switching mechanism which limits the approach of brain like energy efficiency. Different from them, organic memristive devices (OMDs) have a narrow switching window and implement neuromorphic characteristics at voltages <= 1 V. Despite OMDs potentialities in bioinspired electronics, guidelines for the design of devices and materials are still missing. Here it is shown that the device capacitance represents a significant degree of freedom for targeting devices applications. It is also shown that a single OMD emulates activity dependent synaptic functions and neuronal temporal and spatial summation, taking advantage of its three-terminals configuration. Interestingly, despite the neuromorphic applications, OMDs can also sense and amplify incoming signals on the basis of their capacitive and/or resistive values. This spectrum of applications, ranging from volatile to non-volatile characteristics and from neuromorphic computing to bio signals sensing, sets the stage for the realization of integrated circuits for adaptive sensing

Aerosol Jet Printed Organic Memristive Microdevices Based on a Chitosan:PANI Composite Conductive Channel

In this study we show a chitosan:polyaniline (CPA)-based ink, responding to eco-biofriendly criteria, specifically developed for the manufacturing of the first organic memristive device (OMD) with an aerosol jet printed conductive channel. Our contribution is in the context of bioelectronics, where there is an increasing interest in emulating neuro-morphic functions. In this framework, memristive devices and systems have been shown to be well suited. In particular organic-based devices are envisaged as very promising in some applications, such as brain-machine interfacing, owing to specific properties of organics (e.g., biocompatibility, mixed ionic-electronic conduction). On the other hand, the research activities on flexible organic (bio)electronic devices and direct writing (DW) noncontact techniques increasingly overlap in the effort of achieving reliable applications benefiting from the rapid prototyping to accomplish a fast device optimization. In this context, ink-based techniques, such as aerosol jet printing (AJP), although particularly well suited to implement 3D-printed electronics due to advantages it offers in terms of a wide set of allowed printable materials, still require research efforts aimed at conferring printability to the desired precursors. The developed CPA composite was characterized by FTIR, DLS, and MALDI-TOF techniques, while the related aerosol jet printed films were studied by SEM and profilometry. Taking advantage of the intrinsic and stable electrical conductivity of CPA films, which do not necessarily require any acidic treatment to promote a sustained charge carrier conduction, 10 mu m short-channel OMDs were hence manufactured by interfacing the printed CPA layers with a solid polyelectrolyte (SPE). We accordingly demonstrated prototypes of stable and best performing OMD devices with downscaled features, showing well-defined counterclockwise hysteresis/rectification and an enhanced durability. These properties pave the way to further improving performance, as well as to realizing a direct integration of the devices into hardware neural networks by in-line fabrication routes

A biofunctional polymeric coating for microcantilever molecular recognition

An innovative route to activate silicon microcantilevers (MCs) for label free molecular recognition is presented. The method consists in coating the underivatized MCs with a functional ter-polymer based on N,N-dimethylacrylamide (DMA) bearing N-acryloyloxysuccinimide (NAS) and 3-(trimethoxysilyl)propyl-methacrylate (MAPS), two functional monomers that confer to the polymer the ability to react with nucleophilic species on biomolecules and with glass silanols, respectively. The polymer was deposited onto MCs by dip coating. Polymer coated MCs were tested in both static and dynamic modes of actuation, featuring detection of DNA hybridization as well as protein/protein interaction. In the dynamic experiments, focused on protein detection, the MCs showed an average mass responsivity of 0.4 Hz/pg for the first resonant mode and of 2.5 Hz/pg for the second resonant mode. The results of the static experiments, dedicated to DNA hybridization detection, allowed for direct estimation of the DNA duplex formation energetics, which resulted fully consistent with the nominal expected values. These results, together with easiness and cheapness, high versatility, and excellent stability of the recognition signal, make the presented route a reliable alternative to standard SAM functionalization (for microcantilevers (MCs) and for micro–electro–mechanical systems (MEMS) in general)

Combination of Organic-Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification

Nowadays, neuromorphic systems based on memristors are considered promising approaches to the hardware realization of artificial intelligence systems with efficient information processing. However, a major bottleneck in the physical implementation of these systems is the strong dependence of their performance on the unavoidable variations (cycle-to-cycle, c2c, or device-to-device, d2d) of memristive devices. Recently, reservoir computing (RC) and spiking neuromorphic systems (SNSs) are separately proposed as valuable options to partially mitigate this problem. Herein, both approaches are combined to create a fully organic system based on 1) volatile polyaniline memristive devices for the reservoir layer and 2) nonvolatile parylene memristors for the SNS readout layer. This combination provides a simpler SNS training procedure compared with the formal neural networks and results in greater robustness to device variability, while ensuring the extraction and encoding of the input critical features (performed by the polyaniline reservoir) and the analysis and classification performed by the SNS layer. Furthermore, the spatiotemporal pattern recognition of the system brings us closer to the implementation of efficient and reliable brain-inspired computing systems built with partially unreliable analog elements