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The main goal of the field of neuromorphic computing is to build machines that emulate aspects of the brain in its ability to perform complex tasks in parallel and with great energy efficiency. Thanks to new computing architectures, these machines could revolutionize high-performance computing and find applications to perform local, low-energy computing for sensors and robots. The use of organic and soft materials in neuromorphic computing is appealing in many respects, for instance, because it allows better integration with living matter to seamlessly meld sensing with signal processing, and ultimately, stimulation in a closed-feedback loop. Indeed, not only can the mechanical properties of organic materials match those of tissue, but also, the working mechanisms of these devices involving ions, in addition to electrons, are compatible with human physiology. Another advantage of organic materials is the potential to introduce novel fabrication techniques relying on additive manufacturing amenable to one-of-a-kind form factors. This field is still nascent, therefore many concepts are still being proposed, without a clear winner. Furthermore, the field of application of organic neuromorphics, where bioinspiration and biointegration are extremely appealing, calls for a co-design approach from materials to systems. 

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies. 

Abstract In organic mixed ionic–electronic conductors (OMIECs), it is critical to understand the motion of ions in the electrolyte and OMIEC. Generally, the focus is on the movement of net charge during gating, and the motion of neutral anion–cation pairs is seldom considered. Uptake of mobile ion pairs by the semiconductor before electrochemical gating (passive uptake) can be advantageous as this can improve device speed, and both ions can participate in charge compensation during gating. Here, such passive ion pair uptake in high‐speed solid‐state devices is demonstrated using an ion gel electrolyte. This is compared to a polymerized ionic liquid (PIL) electrolyte to understand how ion pair uptake affects device characteristics. Using X‐ray photoelectron spectroscopy, the passive uptake of ion pairs from the ion gel into the OMIEC is detected, whereas no uptake is observed with a PIL electrolyte. This is corroborated by X‐ray scattering, which reveals morphological changes to the OMIEC from the uptake of ion pairs. With in situ Raman, a reorganization of both anions and cations is then observed during gating. Finally, the speed and retention of OMIEC‐based neuromorphic devices are tuned by controlling the freedom of charge motion in the electrolyte. 

Abstract Cell membranes are fundamental for cellular function as they protect the cell and control passage in and out of the cell. Despite their clear significance, cell membranes are often difficult to study, due to their complexity and the lack of available technologies to interface with them and transduce their functions. Overcoming this complexity by developing simple, reductionist models can facilitate their study. Indeed, lipid layers represent a simplified yet representative model for a cell membrane. Lipid layers are highly insulating, a property that is directly affected by changes in lipid packing or membrane fluidity. Such physical changes in the membrane models can be characterized by coupling them with an electronic transducer. Herein, a lipid monolayer that is stabilized between two immiscible solvents is integrated with an organic electrochemical transistor, which is capable of operating in a biphasic solvent mixture. The platform is used to evaluate how lidocaine, a widely used anesthetic the working mechanism of which is still a matter of debate, interacts with the cell membrane. The present study provides evidence that the anesthetic directly interacts with the lipids in the membrane, affecting their packing and therefore the monolayer permeability. The proposed platform provides an elegant solution for studying compound–membrane interactions.