Cognitive computing has brought about the possibility of computer programs with ability to learn by recognizing patterns in data and to make predictions on the learned dependencies by mimicking the operation of the human brain. However, their energy efficiency is still orders of magnitude below the biological counterpart as the current linear Boolean logic is ill-suited for the simulation of the huge arrays of interconnected neurons. Neuromorphic computing aims to greatly improve the efficiency by emulating the synaptic functionality and interconnectivity on the hardware level. In biological neural networks, communication between neurons is facilitated by synapses that modulate the signal through changes in the synaptic weight. The time-variability of these operations is thought to allow the single node to both process and store information.
RESWITCH seeks to emulate this synaptic plasticity by exploiting the coupled ionic/electronic transport in redox-active hybrid metal-organic coordination polymer thin films. As in fully organic conjugated polymers, the electronic conductivity can be modulated electrochemically with dynamic operation arising from the concurrent counter-ion motion. The interplay of the metal and organic constituents allows for precise control of the electric/electrochemical properties, but poor processability limits their applicability for nanotechnology applications. In RESWITCH, a new thin film-based approach is implemented with Molecular Layer Deposition (MLD). A library of MLD processes for high-quality, ultra-thin films of redox-active and electrically conductive materials will be established. The thin film approach allows for detailed exploration on the contribution of the metal and organic components to the redox-properties and conductivity. The ultimate target is to implement the thin films in a novel bilayer this film device in which the conductance can be controlled dynamically with electrochemical doping of the adjacent layers.