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Neural network training can be slow and energy-expensive due to the frequent transfer of weight data between digital memory and processing units. Neuromorphic systems can accelerate neural networks by performing multiply-accumulate operations in parallel using nonvolatile analog memory. However, executing the widely used backpropagation training algorithm in multilayer neural networks requires information-and therefore storage-of the partial derivatives of the weight values preventing suitable and scalable implementation in hardware. Here, we propose a hardware implementation of the backpropagation algorithm that progressively updates each layer using in situ stochastic gradient descent, avoiding this storage requirement. We experimentally demonstrate the in situ error calculation and the proposed progressive backpropagation method in a multilayer hardware-implemented neural network. We confirm identical learning characteristics and classification performance compared to conventional backpropagation in software. We show that our approach can be scaled to large and deep neural networks, enabling highly efficient training of advanced artificial intelligence computing systems.

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.

The slowing-down of Moore’s law is shifting the computing paradigm towards solutions based on quantum and neuromorphic computing elements. Unlike conventional digital computing, neuromorphic computing is based on analog devices. In this work, we propose a three-terminal neuromorphic organic device (NODe) capable of providing both analog computing and memory in a single device by tuning its conductance. The availability of three-terminal devices enables the independent tuning of the NODes, preventing write sneak path issues typical of the two-terminal memristor crossbar array. The NODe conductance relaxes exponentially with a measured time constant of 2.9 h, furthermore, it can be operated at 51 MHz, corresponding to an estimated energy efficiency of 0.1 pJ per multiply-accumulate (MAC) operation. To demonstrate the NODe’s computing capabilities, a 3×3 crossbar array has been successfully used to perform edge detection and blurring on an image with 128×64 pixels.

Neuromorphic systems that display synaptic conditioning based on biochemical signaling activity have recently been introduced in the form of artificial synapses that are model devices to develop tissue-interfaced platforms. In this regard, biohybrid synapses promise adaptive neuron-integrated functions. However, these systems suffer from both molecular cross-talk as biological neural circuits signal transmission typically involves more than one neuromodulator, and unstable electronics wirings as complex architectures are required to interface the tissues. Moreover, whilst novel spiking circuits can work as artificial neurons, they only recreate the biological electrical signaling pathway while electrochemical signal transduction is required for inter-neuron communication. As such, artificial chemically-mediated synapses are essential to perform memory/learning computing functions. Herein, an electrochemical neuromorphic organic device (ENODe) working as an artificial synapse that overcomes electrochemical and readout interferences while it emulates two neurotransmitters synaptic weight modulation and their recycling machinery at the synaptic cleft is shown. Neuronal short- and long-term plasticity are replicated by transducing two separate neurotransmitter-mediated chemical signals into reversible and nonreversible variations of PEDOT:PSS conductance. By exploiting the electrochromic properties of PEDOT:PSS, an alternative optical monitoring strategy is introduced which promises stable multidevice readout from complex bio-hybrid interfaces. The platform emulates high-order biological processes such as intrinsic forgetting, memory consolidation, and neurotransmitter co-modulation. These brain-inspired functionalities herald the development of tissue-integrated neuromorphic systems that combine spiking (electrical neurons) and nonspiking (electrochemical synapses) elements, thus envisioning prosthetic bridges for neural engineering and regenerative medicine.

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.

Magnetic microbeads have been widely used for the capturing of biomarkers, as well as for microfluidic mixing for point-of-care diagnostics. In magnetic micromixing, microbead motion is generated by external electromagnets, inducing fluid kinetics, and consequently mixing. Here, we utilize an in-plane rotating magnetic field to induce magnetic bead mixing in a circular microfluidic chamber that allows better access with (optical) readout than for existing micromixing approaches. We analyze the magnetic bead dynamics, the induced fluid profiles and we quantify the mixing performance of the system. The rotating field causes the combination of (1) a global rotating flow counter to the external field rotation induced by magnetic particles moving along the chamber side wall, with (2) local flow perturbations induced by rotating magnetic bead clusters in the central area of the chamber, rotating in the same direction as the external field. This combination leads to efficient mixing performance within 2 min of actuated magnetic field. We integrate magnetic mushroom-shaped features around the circumference of the chamber to generate significantly higher global fluid velocities compared with the no-mushroom configuration, but this results in less efficient mixing due to the absence of the central rotating bead clusters. To validate and understand the experimental results and to predict further enhancement of mixing, we carry out numerical simulations of induced fluid profiles and their corresponding mixing indices, and we explore the additional effect of integrating geometrical structures. The micromixing method we introduce here is particularly suitable for microfluidic devices in which the biochemical assay happens in a microfluidic chamber under no-flow conditions, i.e., with initially stagnant fluids, and for which the time-to-result is critical, such as in point-of-care diagnostics.

Organic mixed ionic–electronic conductors (OMIECs) are central to bioelectronic applications such as biosensors, health-monitoring devices, and neural interfaces, and have facilitated efficient next-generation brain-inspired computing and biohybrid systems. Despite these examples, smart and adaptive circuits that can locally process and optimize biosignals have not yet been realized. Here, a tunable sensing circuit is shown that can locally modulate biologically relevant signals like electromyograms (EMGs) and electrocardiograms (ECGs), that is based on a complementary logic inverter combined with a neuromorphic memory element, and that is constructed from a single polymer mixed conductor. It is demonstrated that a small neuromorphic array based on this material effects high classification accuracy in heartbeat anomaly detection. This high-performance material allows for straightforward monolithic integration, which reduces fabrication complexity while also achieving high on/off ratios with excellent ambient p- and n-type stability in transistor performance. This material opens a route toward simple and straightforward fabrication and integration of more sophisticated adaptive circuits for future smart bioelectronics.

Organic electrochemical transistors (OECTs) have emerged as building blocks for low power circuits, biosensors, and neuromorphic computing. While p-type polymer materials for OECTs are well developed, the choice of high-performance n-type polymers is limited, despite being essential for cation and metabolite biosensors, and crucial for constructing complementary circuits. N-type conjugated polymers that have efficient ion-to-electron transduction are highly desired for electrochemical applications. In this contribution, three non-fused, planar naphthalenediimide (NDI)-dialkoxybithiazole (2Tz) copolymers, which systematically increase the amount of polar tri(ethylene glycol) (TEG) side chains: PNDI2OD-2Tz (0 TEG), PNDIODTEG-2Tz (1 TEG), PNDI2TEG-2Tz (2 TEG), are reported. It is demonstrated that the OECT performance increases with the number of TEG side chains resulting from the progressively higher hydrophilicity and larger electron affinities. Benefiting from the high electron mobility, excellent ion conduction capability, efficient ion-to-electron transduction, and low-lying lowest unoccupied molecular orbital energy level, the 2 TEG polymer achieves close to 105 on-off ratio, fast switching, 1000 stable operation cycles in aqueous electrolyte, and has a long shelf life. Moreover, the higher number TEG chain substituted polymer exhibits good conductance state retention over two orders of magnitudes in electrochemical resistive random-access memory devices, highlighting its potential for neuromorphic computing.

In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.

Recent global events have distinctly demonstrated the need for fast diagnostic analysis of targets in a liquid sample. However, microfluidic lab-on-a-chip devices for point-of-care diagnostics can suffer from slow analysis due to poor mixing. Here, we experimentally explore the mixing effect within a microfluidic chamber, as obtained from superparamagnetic beads exposed to an out-of-plane (vertical) rotating magnetic field. Various magnetic protocols are explored, and the level of sample homogeneity is measured by determining the mixing efficiency index. In particular, we introduce a method to induce effective mixing in a microfluidic chamber by the actuation of the same beads to perform global swarming behavior, a collective motion of a large number of individual entities often seen in nature. The microparticle swarming induces high fluid velocities in initially stagnant fluids, and it can be externally controlled. The method is pilot-tested using a point-of-care test featuring a bioluminescent assay for the detection of antibodies. The mixing by the magnetic beads leads to increased assay kinetics, which indeed reduces the time to sensor readout substantially. Magnetic microparticle swarming is expected to be beneficial for a wide variety of point-of-care devices, where fast homogeneity of reagents does play a role.

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.

Organic electrochemical transistors (OECTs) show great promise for flexible, low-cost, and low-voltage sensors for aqueous solutions. The majority of OECT devices are made using the polymer blend poly(ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), in which PEDOT is intrinsically doped due to inclusion of PSS. Because of this intrinsic doping, PEDOT:PSS OECTs generally operate in depletion mode, which results in a higher power consumption and limits stability. Here, a straightforward method to de-dope PEDOT:PSS using commercially available amine-based molecular de-dopants to achieve stable enhancement-mode OECTs is presented. The enhancement-mode OECTs show mobilities near that of pristine PEDOT:PSS (≈2 cm2 V−1 s−1) with stable operation over 1000 on/off cycles. The electron and proton exchange among PEDOT, PSS, and the molecular de-dopants are characterized to reveal the underlying chemical mechanism of the threshold voltage shift to negative voltages. Finally, the effect of the de-doping on the microstructure of the spin-cast PEDOT:PSS films is investigated.

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.

A variety of optical applications rely on the absorption and reemission of light. The quantum yield of this process often plays an essential role. When the quantum yield deviates from unity by significantly less than 1%, applications such as luminescent concentrators and optical refrigerators become possible. To evaluate such high performance, we develop a measurement technique for luminescence efficiency with sufficient accuracy below one part per thousand. Photothermal threshold quantum yield is based on the quantization of light to minimize overall measurement uncertainty. This technique is used to guide a procedure capable of making ensembles of near-unity emitting cadmium selenide/cadmium sulfide (CdSe/CdS) core-shell quantum dots. We obtain a photothermal threshold quantum yield luminescence efficiency of 99.6 ± 0.2%, indicating nearly complete suppression of nonradiative decay channels.

Bioelectronics has made enormous progress towards the development of concepts, materials and devices that are capable of sensing, monitoring and controlling a biological environment, by incorporating concepts such as local drug delivery and electrical, chemical or mechanical stimulation. Nevertheless, fully autonomous bioelectronic applications demand not only the acquisition of biological signals, but also local low power data processing, storage and the extraction of specific features of merit. Here, we present an overview of the latest studies on organic neuromorphic and sensing devices. We also speculate on the need for smart and adaptive sensing and highlight the potential of these concepts to enhance the interaction efficiency between electronics and biological substances.

Microfluidic mixing becomes a necessity when thorough sample homogenization is required in small volumes of fluid, such as in lab-on-a-chip devices. For example, efficient mixing is extraordinarily challenging in capillary-filling microfluidic devices and in microchambers with stagnant fluids. To address this issue, specifically designed geometrical features can enhance the effect of diffusion and provide efficient mixing by inducing chaotic fluid flow. This scheme is known as “passive” mixing. In addition, when rapid and global mixing is essential, “active” mixing can be applied by exploiting an external source. In particular, magnetic mixing (where a magnetic field acts to stimulate mixing) shows great potential for high mixing efficiency. This method generally involves magnetic beads and external (or integrated) magnets for the creation of chaotic motion in the device. However, there is still plenty of room for exploiting the potential of magnetic beads for mixing applications. Therefore, this review article focuses on the advantages of magnetic bead mixing along with recommendations on improving mixing in low Reynolds number flows (Re ≤ 1) and in stagnant fluids.

Poly(3,4-ethylenedioxythiophene) blended with polystyrenesulfonate and poly(styrenesulfonic acid), PEDOT:PSS, has found widespread use in organic electronics. Although PEDOT:PSS is commonly used in its doped electrically conducting state, the ability to efficiently convert PEDOT:PSS to its undoped nonconducting state is of interest for a wide variety of applications ranging from biosensors to organic neuromorphic devices. Exposure to aliphatic monoamines, acting as an electron donor and Brønsted-Lowry base, has been reported to be partly successful, but monoamines are unable to fully dedope PEDOT:PSS. Remarkably, some - but not all - polyamines can dedope PEDOT:PSS very efficiently to very low conductivity levels, but the exact chemical mechanism involved is not understood. Here, we study the dedoping efficacy of 21 different aliphatic amines. We identify the presence of two or more primary amines, which can participate in an intramolecular reaction, as the key structural motif that endows polyamines with high PEDOT:PSS dedoping strength. A multistep reaction mechanism, involving sequential electron transfer and deprotonation steps, is proposed that consistently explains the experimental results. Finally, we provide a simple method to convert the commonly used aqueous PEDOT:PSS dispersion into a precursor formulation that forms fully dedoped PEDOT:PSS films after spin coating and subsequent thermal annealing.