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Neuromorphic computing offers the opportunity to curtail the huge energy demands of modern artificial intelligence (AI) applications by implementing computations into new, brain-inspired computing architectures. However, the lack of fabrication processes able to integrate several computing units into monolithic systems and the need for new, hardware-tailored training algorithms still limit the scope of application and performance of neuromorphic hardware. Recent advancements in the field of organic transistors present new opportunities for neuromorphic systems and smart sensing applications, thanks to their unique properties such as neuromorphic behavior, low-voltage operation, and mixed ionic-electronic conductivity. Organic neuromorphic transistors push the boundaries of energy efficient brain-inspired hardware AI, facilitating decentralized on-chip learning and serving as a foundation for the advancement of closed-loop intelligent systems in the next generation. The biocompatibility and dual ionic-electronic conductivity of organic materials introduce new prospects for biointegration and bioelectronics. Their ability to sense and regulate biosystems, as well as their neuro-inspired functions can be combined with neuromorphic computing to create the next-generation of bioelectronics. These systems will be able to seamlessly interact with biological systems and locally compute biosignals in a relevant matter.

Recent developments in organic neuromorphic devices and biohybrid interfaces are promising examples that show potential to improve implantable devices toward organic adaptive brain-machine interfaces. However, fully integrated neuromorphic arrays still require relatively complex circuitry that includes multiple access devices to ensure synaptic weight stability and prevent sneak paths. Here, it is shown that polydopamine (PDA), the byproduct of dopamine autoxidation, promotes proton conductivity and can serve as a solid-state electrolyte. Slow kinetics and high energy barriers of the PDA solid electrolyte prevent loss of conductance state for the device with a three-terminal configuration without an access device, while partial dedoping of the conductive polymer channel by PDA simultaneously increases its stability in ambient environments. Fabricating the neuromorphic device on a flexible poly(styrene-block-isobutylene-block-styrene) substrate and the inherent biocompatibility of PDA demonstrates its potential toward more sophisticated implantable neuromorphic circuits for advanced neuroprosthetics.

Since the introduction of adaptive systems for corrective measures, static and dynamic disturbances have been reduced through the manipulation of surface shape in a well-controlled manner. One important application where adaptive systems are highly needed is in the future photo-lithography machines, particularly the wafer table where disturbances affect overlay and focus performance. To reduce overlay and focus errors, a dense array of actuators must be integrated into the wafer table to apply both in- and out-of-plane corrective deformations to the wafer surface. To realize a certain wafer shape, influence functions are linearly superimposed. Accurate models of the influence functions result in an accurate prediction of the final wafer shape. To reduce calibration errors, the influence function of every actuator needs to be determined. Here, we propose a technique to rapidly measure the influence function: the influence function measurement (IFM) technique. Using the actuate-sense property of piezoelectric materials, the influence function is determined by activating one piezo-actuator and measuring the charge induced on the neighboring piezo-actuators. The measured charges are compared to the results of finite element simulations and the absolute difference of 0.3 % is reported for the inter-actuator charge coupling parameters. This clearly indicates the potential of the proposed technique.

Brain-inspired computing paradigms have led to substantial advances in the automation of visual and linguistic tasks by emulating the distributed information processing of biological systems1. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics2 and brain-machine interfaces3. While promising, these implementations rely on software to run ANN algorithms. Ultimately, it is desirable to build hardware ANNs4,5 that can both directly interface with living tissue and adapt based on biofeedback6,7. The first essential step towards biologically integrated neuromorphic systems is to achieve synaptic conditioning based on biochemical signalling activity. Here, we directly couple an organic neuromorphic device with dopaminergic cells to constitute a biohybrid synapse with neurotransmitter-mediated synaptic plasticity. By mimicking the dopamine recycling machinery of the synaptic cleft, we demonstrate both long-term conditioning and recovery of the synaptic weight, paving the way towards combining artificial neuromorphic systems with biological neural networks.

Functional emulation of biological synapses using electronic devices is regarded as the first step toward neuromorphic engineering and artificial neural networks (ANNs). Electrolyte-gated transistors (EGTs) are mixed ionic-electronic conductivity devices capable of efficient gate-channel capacitance coupling, biocompatibility, and flexible architectures. Electrolyte gating offers significant advantages for the realization of neuromorphic devices/architectures, including ultralow-voltage operation and the ability to form parallel-interconnected networks with minimal hardwired connectivity. In this review, the most recent developments in EGT-based electronics are introduced with their synaptic behaviors and detailed mechanisms, including short-/long-term plasticity, global regulation phenomena, lateral coupling between device terminals, and spatiotemporal correlated functions. Analog memory phenomena allow for the implementation of perceptron-based ANNs. Due to their mixed-conductivity phenomena, neuromorphic circuits based on EGTs allow for facile interfacing with biological environments. We also discuss the future challenges in implementing low power, high speed, and reliable neuromorphic computing for large-scale ANNs with these neuromorphic devices. The advancement of neuromorphic devices that rely on EGTs highlights the importance of this field for neuromorphic computing and for novel healthcare technologies in the form of adaptable or trainable biointerfacing.