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 neuromorphic 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 μm 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.