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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. 

Recent breakthroughs in artificial neural networks (ANNs) have spurred interest in efficient computational paradigms where the energy and time costs for training and inference are reduced. One promising contender for efficient ANN implementation is crossbar arrays of resistive memory elements that emulate the synaptic strength between neurons within the ANN. Organic nonvolatile redox memory has recently been demonstrated as a promising device for neuromorphic computing, offering a continuous range of linearly programmable resistance states and tunable electronic and electrochemical properties, opening a path toward massively parallel and energy efficient ANN implementation. However, one of the key issues with implementations relying on electrochemical gating of organic materials is the state‐retention time and device stability. Here, revealed are the mechanisms leading to state loss and cycling instability in redox‐gated neuromorphic devices: parasitic redox reactions and out‐diffusion of reducing additives. The results of this study are used to design an encapsulation structure which shows an order of magnitude improvement in state retention and cycling stability for poly(3,4‐ethylenedioxythiophene)/polyethyleneimine:poly(styrene sulfonate) devices by tuning the concentration of additives, implementing a solid‐state electrolyte, and encapsulating devices in an inert environment. Finally, a comparison is made between programming range and state retention to optimize device operation.

 

We explore conditions for achieving laser liftoff in epitaxially grown heterojunctions, in which single crystal thin films can be separated from their growth substrates using a selectively absorbing buried intermediate layer. Because this highly non‐linear process is subject to a variety of process instabilities, it is essential to accurately characterize the parameters resulting in liftoff. Here, we present an InP/InGaAs/InP heterojunction as a model system for such characterization. We show separation of InP thin films from single crystal InP growth substrates, wherein a ≈10 ns, Nd:YAG laser pulse selectively heats a coherently strained, buried InGaAs layer. We develop a technique to measure liftoff threshold fluences within an inhomogeneous laser spatial profile, and apply this technique to determine threshold fluences of the order 0.5 J cm⁻²for our specimens. We find that the fluence at the InGaAs layer is limited by non‐linear absorption and InP surface damage at high powers, and measure the energy transmission in an InP substrate from 0 to 8 J cm⁻². Characterization of the ejected thin films shows crack‐free, single crystal InP. Finally, we present evidence that the hot InGaAs initiates a liquid phase front that travels into the InP substrate during liftoff.