An Evolvable Organic Electrochemical Transistor for Neuromorphic Applications

Modulating Neuromorphic Behavior of Organic Synaptic Electrolyte-Gated Transistors Through Microstructure Engineering and Potential Applications

Organic synaptic transistors are a promising technology for advanced electronic devices with simultaneous computing and memory functions and for the application of artificial neural networks. In this study, the neuromorphic electrical characteristics of organic synaptic electrolyte-gated transistors are correlated with the microstructural and interfacial properties of the active layers. This is accomplished by utilizing a semiconducting/insulating polyblend-based pseudobilayer with embedded source and drain electrodes, referred to as PB-ESD architecture. Three variations of poly(3-hexylthiophene) (P3HT)/poly(methyl methacrylate) (PMMA) PB-ESD-based organic synaptic transistors are fabricated, each exhibiting distinct microstructures and electrical characteristics, thus serving excellent samples for exploring the critical factors influencing neuro-electrical properties. Poor microstructures of P3HT within the active layer and a flat active layer/ion-gel interface correspond to typical neuromorphic behaviors such as potentiated excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and short-term potentiation (STP). Conversely, superior microstructures of P3HT and a rough active layer/ion-gel interface correspond to significantly higher channel conductance and enhanced EPSC and PPF characteristics as well as long-term potentiation behavior. Such devices were further applied to the simulation of neural networks, which produced a good recognition accuracy. However, excessive PMMA penetration into the P3HT conducting channel leads to features of a depressed EPSC and paired-pulse depression, which are uncommon in organic synaptic transistors. The inclusion of a second gate electrode enables the as-prepared organic synaptic transistors to function as two-input synaptic logic gates, performing various logical operations and effectively mimicking neural modulation functions. Microstructure and interface engineering is an effective method to modulate the neuromorphic behavior of organic synaptic transistors and advance the development of bionic artificial neural networks.

Controlled Migration of Lithium Cations by Diamine Bridges in Water-Processable Polymer-Based Solid-State Electrolyte Memory Layers for Organic Synaptic Transistors

Synaptic transistors require sufficient retention (memory) performances of current signals to exactly mimic biological synapses. Ion migration has been proposed to achieve high retention characteristics but less attention has been paid to polymer-based solid-state electrolytes (SSEs) for organic synaptic transistors (OSTRs). Here, OSTRs with water-processable polymer-based SSEs, featuring ion migration-controllable molecular bridges, which are prepared by reactions of poly(4-styrenesulfonic acid) (PSSA), diethylenetriamine (DETA), and lithium hydroxide (LiOH) are demonstrated. The ion conductivity of PSSA:LiOH:DETA (1:0.4:X, PLiD) films is remarkably changed by the molar ratio (X) of DETA, which is attributed to the extended distances between the PSSA chains by the DETA bridges. The devices with the PLiD layers deliver noticeably changed hysteresis reaching an optimum at X = 0.2, leading to the longest retention of current signals upon single/double pulses. The long-term potentiation test confirms that the present OSTRs can gradually build up the postsynaptic current by gate pulses of -2 V, while the long-term depression can be adjusted by varying the depression gate pulses (≈0.2-1.2 V). The artificial neural network simulations disclose that the present OSTRs with the ion migration-controlled PLiD layers can perform synaptic processes with an accuracy of ≈96%.

Transient Response and Ionic Dynamics in Organic Electrochemical Transistors

The rapid development of organic electrochemical transistors (OECTs) has ushered in a new era in organic electronics, distinguishing itself through its application in a variety of domains, from high-speed logic circuits to sensitive biosensors, and neuromorphic devices like artificial synapses and organic electrochemical random-access memories. Despite recent strides in enhancing OECT performance, driven by the demand for superior transient response capabilities, a comprehensive understanding of the complex interplay between charge and ion transport, alongside electron-ion interactions, as well as the optimization strategies, remains elusive. This review aims to bridge this gap by providing a systematic overview on the fundamental working principles of OECT transient responses, emphasizing advancements in device physics and optimization approaches. We review the critical aspect of transient ion dynamics in both volatile and non-volatile applications, as well as the impact of materials, morphology, device structure strategies on optimizing transient responses. This paper not only offers a detailed overview of the current state of the art, but also identifies promising avenues for future research, aiming to drive future performance advancements in diversified applications.

PEDOT:PSS-Based Prolonged Long-Term Decay Synaptic OECT with Proton-Permeable Material, Nafion

Poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), a conductive polymer, has gained popularity as the channel layer in organic electrochemical transistors (OECTs) due to its high conductivity and straightforward processing. However, difficulties arise in controlling its conductivity through gate voltage, presenting a challenge. To address this issue, aromatic amidine base, diazabicyclo[4.3.0]non-5-ene (DBN), is used to stabilize the doping state of the PEDOT chain through a reliable chemical de-doping process. Furthermore, the addition of the proton-penetrable material Nafion to the PEDOT:PSS channel layer induces phase separation between the substances. By utilizing a solution containing both PEDOT:PSS and Nafion as the channel layer of OECTs, the efficiency of ion movement into the channel from the electrolyte is enhanced, resulting in improved OECT performance. The inclusion of Nafion in the OECTs' channel layer modifies ion movement dynamics, allowing for the adjustment of synaptic properties such as pulse-paired facilitation, memory level, short-term plasticity, and long-term plasticity. This research aims to introduce new possibilities in the field of neuromorphic computing and contribute to biomimetic technology through the enhancement of electronic component performance.

Information Transmission through Biotic-Abiotic Interfaces to Restore or Enhance Human Function

Advancements in reliable information transfer across biotic-abiotic interfaces have enabled the restoration of lost human function. For example, communication between neuronal cells and electrical devices restores the ability to walk to a tetraplegic patient and vision to patients blinded by retinal disease. These impactful medical achievements are aided by tailored biotic-abiotic interfaces that maximize information transfer fidelity by considering the physical properties of the underlying biological and synthetic components. This Review develops a modular framework to define and describe the engineering of biotic and abiotic components as well as the design of interfaces to facilitate biotic-abiotic information transfer using light or electricity. Delineating the properties of the biotic, interface, and abiotic components that enable communication can serve as a guide for future research in this highly interdisciplinary field. Application of synthetic biology to engineer light-sensitive proteins has facilitated the control of neural signaling and the restoration of rudimentary vision after retinal blindness. Electrophysiological methodologies that use brain-computer interfaces and stimulating implants to bypass spinal column injuries have led to the rehabilitation of limb movement and walking ability. Cellular interfacing methodologies and on-chip learning capability have been made possible by organic transistors that mimic the information processing capacity of neurons. The collaboration of molecular biologists, material scientists, and electrical engineers in the emerging field of biotic-abiotic interfacing will lead to the development of prosthetics capable of responding to thought and experiencing touch sensation via direct integration into the human nervous system. Further interdisciplinary research will improve electrical and optical interfacing technologies for the restoration of vision, offering greater visual acuity and potentially color vision in the near future.

Vertical organic electrochemical transistor platforms for efficient electropolymerization of thiophene based oligomers

Organic electrochemical transistors (OECTs) have emerged as promising candidates for various fields, including bioelectronics, neuromorphic computing, biosensors, and wearable electronics. OECTs operate in aqueous solutions, exhibit high amplification properties, and offer ion-to-electron signal transduction. The OECT channel consists of a conducting polymer, with PEDOT:PSS receiving the most attention to date. While PEDOT:PSS is highly conductive, and benefits from optimized protocols using secondary dopants and detergents, new p-type and n-type polymers are emerging with desirable material properties. Among these, low-oxidation potential oligomers are highly enabling for bioelectronics applications, however the polymers resulting from their polymerization lag far behind in conductivity compared with the established PEDOT:PSS. In this work we show that by careful design of the OECT geometrical characteristics, we can overcome this limitation and achieve devices that are on-par with transistors employing PEDOT:PSS. We demonstrate that the vertical architecture allows for facile electropolymerization of a family of trimers that are polymerized in very low oxidation potentials, without the need for harsh chemicals or secondary dopants. Vertical and planar OECTs are compared using various characterization methods. We show that vOECTs are superior platforms in general and propose that the vertical architecture can be expanded for the realization of OECTs for various applications.

Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review

Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing

Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.

High-Endurance Long-Term Potentiation in Neuromorphic Organic Electrochemical Transistors by PEDOT:PSS Electrochemical Polymerization on the Gate Electrode

The brain exhibits extraordinary information processing capabilities thanks to neural networks that can operate in parallel with minimal energy consumption. Memory and learning require the creation of new neural networks through the long-term modification of the structure of the synapses, a phenomenon called long-term plasticity. Here, we use an organic electrochemical transistor to simulate long-term potentiation and depotentiation processes. Similarly to what happens in a synapse, the polymerization of the 3,4-ethylenedioxythiophene (EDOT) on the gate electrode modifies the structure of the device and boosts the ability of the gate potential to modify the conductivity of the channel. Operando AFM measurements were carried out to demonstrate the correlation between neuromorphic behavior and modification of the gate electrode. Long-term enhancement depends on both the number of pulses used and the gate potential, which generates long-term potentiation when a threshold of +0.7 V is overcome. Long-term depotentiation occurs by applying a +3.0 V potential and exploits the overoxidation of the deposited PEDOT:PSS. The induced states are stable for at least 2 months. The developed device shows very interesting characteristics in the field of neuromorphic electronics.

Iontronic memories based on ionic redox systems: operation protocols

A recently developed, new ionic device called the ionic voltage effect soft triode (IVEST) was optimized, tuned and embedded into a memory application concept. The device is an electrochemical micro-cell, consisting of a top electrode and two bottom electrodes. The device controls the concentration and diffusion of ions via the voltage applied on the top electrode. The device showed a memory effect lasting up to 6 hours. Despite the remarkably large stability time, the memory contrast was small in the first device versions. Now, we have increased the memory contrast by introducing a new external electrical circuit layout combined with a new operation protocol. This new investigation also reveals peculiarities of the memory and shows that the IVEST can be used in memory applications. These iontronic memories show a secondary information storage connected with the read-out frequency.

Emerging Opportunities for 2D Materials in Neuromorphic Computing

Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold great promise for high-performance neuromorphic computing devices with the advantages of high energy efficiency and integration density. This article provides a comprehensive overview of various 2D materials, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), and black phosphorus (BP), for neuromorphic computing applications. The potential of these materials in neuromorphic computing is discussed from the perspectives of material properties, growth methods, and device operation principles.

Stable organic electrochemical neurons based on p-type and n-type ladder polymers

Organic electrochemical transistors (OECTs) are a rapidly advancing technology that plays a crucial role in the development of next-generation bioelectronic devices. Recent advances in p-type/n-type organic mixed ionic-electronic conductors (OMIECs) have enabled power-efficient complementary OECT technologies for various applications, such as chemical/biological sensing, large-scale logic gates, and neuromorphic computing. However, ensuring long-term operational stability remains a significant challenge that hinders their widespread adoption. While p-type OMIECs are generally more stable than n-type OMIECs, they still face limitations, especially during prolonged operations. Here, we demonstrate that simple methylation of the pyrrole-benzothiazine-based (PBBT) ladder polymer backbone results in stable and high-performance p-type OECTs. The methylated PBBT (PBBT-Me) exhibits a 25-fold increase in OECT mobility and an impressive 36-fold increase in μC* (mobility × volumetric capacitance) compared to the non-methylated PBBT-H polymer. Combining the newly developed PBBT-Me with the ladder n-type poly(benzimidazobenzophenanthroline) (BBL), we developed complementary inverters with a record-high DC gain of 194 V V-1 and excellent stability. These state-of-the-art complementary inverters were used to demonstrate leaky integrate-and-fire type organic electrochemical neurons (LIF-OECNs) capable of biologically relevant firing frequencies of about 2 Hz and of operating continuously for up to 6.5 h. This achievement represents a significant improvement over previous results and holds great potential for developing stable bioelectronic circuits capable of in-sensor computing.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

New Chemical Dopant and Counterion Mechanism for Organic Electrochemical Transistors and Organic Mixed Ionic-Electronic Conductors

Organic mixed ionic-electronic conductors (OMIECs) have varied performance requirements across a diverse application space. Chemically doping the OMIEC can be a simple, low-cost approach for adapting performance metrics. However, complex challenges, such as identifying new dopant materials and elucidating design rules, inhibit its realization. Here, these challenges are approached by introducing a new n-dopant, tetrabutylammonium hydroxide (TBA-OH), and identifying a new design consideration underpinning its success. TBA-OH behaves as both a chemical n-dopant and morphology additive in donor acceptor co-polymer naphthodithiophene diimide-based polymer, which serves as an electron transporting material in organic electrochemical transistors (OECTs). The combined effects enhance OECT transconductance, charge carrier mobility, and volumetric capacitance, representative of the key metrics underpinning all OMIEC applications. Additionally, when the TBA+ counterion adopts an "edge-on" location relative to the polymer backbone, Coulombic interaction between the counterion and polaron is reduced, and polaron delocalization increases. This is the first time such mechanisms are identified in doped-OECTs and doped-OMIECs. The work herein therefore takes the first steps toward developing the design guidelines needed to realize chemical doping as a generic strategy for tailoring performance metrics in OECTs and OMIECs.

A Biologically Interfaced Evolvable Organic Pattern Classifier

Future brain-computer interfaces will require local and highly individualized signal processing of fully integrated electronic circuits within the nervous system and other living tissue. New devices will need to be developed that can receive data from a sensor array, process these data into meaningful information, and translate that information into a format that can be interpreted by living systems. Here, the first example of interfacing a hardware-based pattern classifier with a biological nerve is reported. The classifier implements the Widrow-Hoff learning algorithm on an array of evolvable organic electrochemical transistors (EOECTs). The EOECTs' channel conductance is modulated in situ by electropolymerizing the semiconductor material within the channel, allowing for low voltage operation, high reproducibility, and an improvement in state retention by two orders of magnitude over state-of-the-art OECT devices. The organic classifier is interfaced with a biological nerve using an organic electrochemical spiking neuron to translate the classifier's output to a simulated action potential. The latter is then used to stimulate muscle contraction selectively based on the input pattern, thus paving the way for the development of adaptive neural interfaces for closed-loop therapeutic systems.

Ultrathin, Soft, Bioresorbable Organic Electrochemical Transistors for Transient Spatiotemporal Mapping of Brain Activity

A critical challenge lies in the development of the next-generation neural interface, in mechanically tissue-compatible fashion, that offer accurate, transient recording electrophysiological (EP) information and autonomous degradation after stable operation. Here, an ultrathin, lightweight, soft and multichannel neural interface is presented based on organic-electrochemical-transistor-(OECT)-based network, with capabilities of continuous high-fidelity mapping of neural signals and biosafety active degrading after performing functions. Such platform yields a high spatiotemporal resolution of 1.42 ms and 20 µm, with signal-to-noise ratio up to ≈37 dB. The implantable OECT arrays can well establish stable functional neural interfaces, designed as fully biodegradable electronic platforms in vivo. Demonstrated applications of such OECT implants include real-time monitoring of electrical activities from the cortical surface of rats under various conditions (e.g., narcosis, epileptic seizure, and electric stimuli) and electrocorticography mapping from 100 channels. This technology offers general applicability in neural interfaces, with great potential utility in treatment/diagnosis of neurological disorders.

In Liquido Computation with Electrochemical Transistors and Mixed Conductors for Intelligent Bioelectronics

Next-generation implantable computational devices require long-term-stable electronic components capable of operating in, and interacting with, electrolytic surroundings without being damaged. Organic electrochemical transistors (OECTs) emerged as fitting candidates. However, while single devices feature impressive figures of merit, integrated circuits (ICs) immersed in common electrolytes are hard to realize using electrochemical transistors, and there is no clear path forward for optimal top-down circuit design and high-density integration. The simple observation that two OECTs immersed in the same electrolytic medium will inevitably interact hampers their implementation in complex circuitry. The electrolyte's ionic conductivity connects all the devices in the liquid, producing unwanted and often unforeseeable dynamics. Minimizing or harnessing this crosstalk has been the focus of very recent studies. Herein, the main challenges, trends, and opportunities for realizing OECT-based circuitry in a liquid environment that could circumnavigate the hard limits of engineering and human physiology, are discussed. The most successful approaches in autonomous bioelectronics and information processing are analyzed. Elaborating on the strategies to circumvent and harness device crosstalk proves that platforms capable of complex computation and even machine learning (ML) can be realized in liquido using mixed ionic-electronic conductors (OMIECs).

Mechanistic and Kinetic Analysis of Perovskite Memristors with Buffer Layers: The Case of a Two-Step Set Process

With the increasing demand for artificially intelligent hardware systems for brain-inspired in-memory and neuromorphic computing, understanding the underlying mechanisms in the resistive switching of memristor devices is of paramount importance. Here, we demonstrate a two-step resistive switching set process involving a complex interplay among mobile halide ions/vacancies (I^-/V_I⁺) and silver ions (Ag⁺) in perovskite-based memristors with thin undoped buffer layers. The resistive switching involves an initial gradual increase in current associated with a drift-related halide migration within the perovskite bulk layer followed by an abrupt resistive switching associated with diffusion of mobile Ag⁺ conductive filamentary formation. Furthermore, we develop a dynamical model that explains the characteristic I-V curve that helps to untangle and quantify the switching regimes consistent with the experimental memristive response. This further insight into the two-step set process provides another degree of freedom in device design for versatile applications with varying levels of complexity.

Electro-optical synaptic characteristics of ferroelectric liquid crystals for artificial intelligence

We propose electro-optical synaptic devices using surface-stabilized ferroelectric liquid crystals. Typical synaptic characteristics were observed for varying pulse time intervals, numbers of pulses, and signal voltages. Plasticity only occurred when pulses were applied at intervals shorter than the response time of the ferroelectric liquid crystal. Moreover, the plasticity increased with a higher pulse voltage and number of pulses. This demonstrates the importance of repeated learning. The synaptic weights required to make connections through learning in an artificial neural network can be determined by tuning the pulse signal. We discuss the high-speed computational potential of optical neuromorphic devices using liquid crystals.

Iontronic analog of synaptic plasticity: Hydrogel-based ionic diode with chemical precipitation and dissolution

In this study, an aqueous nonlinear synaptic element showing plasticity behavior is developed, which is based on the chemical processes in an ionic diode. The device is simple, fully ionic, and easily configurable, requiring only two terminals-for input and output-similar to biological synapses. The key processes realizing the plasticity features are chemical precipitation and dissolution, which occur at forward- or reverse-biased ionic diode junctions in appropriate reservoir electrolytes. Given that the precipitate acts as a physical barrier in the circuit, the above processes change the diode conductivity, which can be interpreted as adjusting "synaptic weight" of the system. By varying the operating conditions, we first demonstrate the four types of plasticity that can be found in biological system: long-term potentiation/depression and short-term potentiation/depression. The plasticity of the proposed iontronic device has characteristics similar to those of neural synapses. To demonstrate its potential use in comparatively complex information processing, we develop a precipitation-based iontronic synapse (PIS) capable of both potentiation and depression. Finally, we show that the postsynaptic signals from the multiple excitatory or inhibitory PISs can be integrated into the total "dendritic" current, which is a function of time and input history, as in actual hippocampal neural circuits.

The Road for 2D Semiconductors in the Silicon Age

Continued reduction in transistor size can improve the performance of silicon integrated circuits (ICs). However, as Moore's law approaches physical limits, high-performance growth in silicon ICs becomes unsustainable, due to challenges of scaling, energy efficiency, and memory limitations. The ultrathin layers, diverse band structures, unique electronic properties, and silicon-compatible processes of 2D materials create the potential to consistently drive advanced performance in ICs. Here, the potential of fusing 2D materials with silicon ICs to minimize the challenges in silicon ICs, and to create technologies beyond the von Neumann architecture, is presented, and the killer applications for 2D materials in logic and memory devices to ease scaling, energy efficiency bottlenecks, and memory dilemmas encountered in silicon ICs are discussed. The fusion of 2D materials allows the creation of all-in-one perception, memory, and computation technologies beyond the von Neumann architecture to enhance system efficiency and remove computing power bottlenecks. Progress on the 2D ICs demonstration is summarized, as well as the technical hurdles it faces in terms of wafer-scale heterostructure growth, transfer, and compatible integration with silicon ICs. Finally, the promising pathways and obstacles to the technological advances in ICs due to the integration of 2D materials with silicon are presented.

An Electrochemical-Electret Coupled Organic Synapse with Single-Polarity Driven Reversible Facilitation-to-Depression Switching

Neuromorphic engineering and artificial intelligence demands hardware elements that emulates synapse algorithms. During the last decade electrolyte-gated organic conjugated materials have been explored as a platform for artificial synapses for neuromorphic computing. Unlike biological synapses, in current devices the synaptic facilitation and depression are triggered by voltages with opposite polarity. To enhance the reliability and simplify the operation of the synapse without lowering its sophisticated functionality, here, an electrochemical-electret coupled organic synapse (EECS) possessing a reversible facilitation-to-depression switch, is devised. Electret charging counterbalances channel conductance changes due to electrochemical doping, inducing depression without inverting the gate polarity. Overall, EECS functions as a threshold-controlled synaptic switch ruled by its amplitude-dependent, dual-modal operation, which can well emulate information storage and erase as in real synapses. By varying the energy level offset between the channel material and the electret, the EECS's transition threshold can be adjusted for specific applications, e.g., imparting additional light responsiveness to the device operation. The novel device architecture represents a major step forward in the development of artificial organic synapses with increased functional complexity and it opens new perspectives toward the fabrication of abiotic neural networks with higher reliability, efficiency, and endurance.

Charge-Compensated N-Doped π-Conjugated Polymers: Toward both Thermodynamic Stability of N-Doped States in Water and High Electron Conductivity

The understanding and applications of electron-conducting π-conjugated polymers with naphtalene diimide (NDI) blocks show remarkable progress in recent years. Such polymers demonstrate a facilitated n-doping due to the strong electron deficiency of the main polymer chain and the presence of the positively charged side groups stabilizing a negative charge of the n-doped backbone. Here, the n-type conducting NDI polymer with enhanced stability of its n-doped states for prospective "in-water" applications is developed. A combined experimental-theoretical approach is used to identify critical features and parameters that control the doping and electron transport process. The facilitated polymer reduction ability and the thermodynamic stability in water are confirmed by electrochemical measurements and doping studies. This material also demonstrates a high conductivity of 10^-2  S cm^-1  under ambient conditions and 10^-1  S cm^-1  in vacuum. The modeling explains the stabilizing effects  for various dopants. The simulations show a significant doping-induced "collapse" of the positively charged side chains on the core bearing a partial negative charge. This explains a decrease in the lamellar spacing observed in experiments. This study fundamentally enables a novel pathway for achieving both thermodynamic stability of the n-doped states in water and the high electron conductivity of polymers.

Electrochemical Fabrication and Characterization of Organic Electrochemical Transistors Using poly(3,4-ethylenedioxythiophene) with Various Counterions

Organic electrochemical transistors (OECTs) are promising bioelectronic devices, especially because of their ability to transport charge both ionically and electronically. Conductive polymers are typically used as the active materials of OECTs. Crosslinked, cast, and dried films of commercially available poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) suspensions are commonly and widely used for OECTs so far. Electrochemical polymerization of PEDOT from 3,4-ethylenedioxythiophene (EDOT) monomer can also be used to fabricate OECTs; however, this approach has not been investigated in as much detail. In particular, the role of various counterions that can be incorporated into the PEDOT films of OECTs has not been systematically studied. Here, we report the electrochemical fabrication and characterization of OECTs using PEDOT with several different counterion salts including lithium perchlorate (LiClO₄), sodium p-toluene sulfonate (pTS), and poly(sodium 4-styrene sulfonate) (PSS). We found that the characteristic dimensions of PEDOT films deposited on the electrodes could be precisely controlled by total charge density, with a nominal thickness of about one micron requiring a current density of about 0.6 C/cm² regardless of the choice of counterion. The films with the PSS counterion were relatively smooth, while PEDOT films prepared with the pTS and LiClO₄ were much rougher due to the sizes of counterions. The PEDOT films with pTS and PSS grew along the substrate surface (in-plane direction) much faster than with LiClO₄. The maximum transconductance (g_m) of a PEDOT OECT was 46 mS with pTS as the counterion with the high on-current level (>10 mA) based on the large channel area. These results provide an effective and efficient way to fabricate OECTs with various monomers and additives as active materials in order to modify the device characteristics for further applications.

Neuro-inspired electronic skin for robots

Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.

Sensitive Detection of Sweat Cortisol Using an Organic Electrochemical Transistor Featuring Nanostructured Poly(3,4-Ethylenedioxythiophene) Derivatives in the Channel Layer

In this study, we examined the influence of functionalized poly(3,4-ethylenedioxythiophene) (PEDOT) nanostructures decorated on the channel layer of an organic electrochemical transistor (OECT) for the detection of sweat cortisol, an adrenocorticosteroid stress hormone. The OECT device featured a bilayer channel confined by a PEDOT:polystyrenesulfonate (PSS) underlayer and a nanostructure-decorated upper layer engineered from the monomers EDOT-COOH and EDOT-EG3 through template-free electrochemical polymerization. This molecular design allowed antibody conjugation using 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide/N-hydroxysulfosuccinimide coupling through the carboxylic acid side chain, with EDOT-EG3 known to minimize nonspecific binding of biomolecules. We also engineered an OECT device having a channel area without any nanostructures to gain insight into the effect of the nanostructures on cortisol sensing. Our new nanostructure-embedded OECT device facilitated real-time detection of cortisol at concentrations ranging from 1 fg/mL to 1 μg/mL with a detection limit of 0.0088 fg/mL with good linearity (R² = 0.9566), in addition to excellent selectivity toward cortisol among other structurally similar interfering compounds and high stability and reproducibility. With its rapid response for the detection of 100 ng/mL cortisol-spiked artificial sweat, this nanostructure-decorated OECT device has potential clinical practicality and utility in wearable sensors for future healthcare applications.

Dynamic Model of the Short-Term Synaptic Behaviors of PEDOT-based Organic Electrochemical Transistors with Modified Shockley Equations

Neuromorphic computing is an emerging area with prospects to break the energy efficiency bottleneck of artificial intelligence (AI). A crucial challenge for neuromorphic computing is understanding the working principles of artificial synaptic devices. As an emerging class of synaptic devices, organic electrochemical transistors (OECTs) have attracted significant interest due to ultralow voltage operation, analog conductance tuning, mechanical flexibility, and biocompatibility. However, little work has been focused on the first-principal modeling of the synaptic behaviors of OECTs. The simulation of OECT synaptic behaviors is of great importance to understanding the OECT working principles as neuromorphic devices and optimizing ultralow power consumption neuromorphic computing devices. Here, we develop a two-dimensional transient drift-diffusion model based on modified Shockley equations for poly(3,4-ethylenedioxythiophene) (PEDOT)-based OECTs. We reproduced the typical transistor characteristics of these OECTs including the unique non-monotonic transconductance-gate bias curve and frequency dependency of transconductance. Furthermore, typical synaptic phenomena, such as excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), and short-term plasticity (STP), are also demonstrated. This work is crucial in guiding the experimental exploration of neuromorphic computing devices and has the potential to serve as a platform for future OECT device simulation based on a wide range of semiconducting materials.

Overoxidation of Intrinsically Conducting Polymers

Intrinsically conducting polymers may undergo significant changes of molecular structure and material properties when exposed to highly oxidizing conditions or very positive electrode potentials, commonly called overoxidation. The type and extent of the changes depend on the experimental conditions and chemical environment. They may proceed already at much lower rates at lower electrode potentials because some of the processes associated with overoxidation are closely related to more or less reversible redox processes employed in electrochemical energy conversion and electrochromism. These changes may be welcome for some applications of these polymers in sensors, extraction, and surface functionalization, but in many cases, the change of properties affects the performance of the material negatively, contributing to material and device degradation. This report presents published examples, experimental observations, and their interpretations in terms of both structural and of material property changes. Options to limit and suppress overoxidation are presented, and useful applications are described extensively.

Poly(3,4-ethylenedioxythiophene)-Based Neural Interfaces for Recording and Stimulation: Fundamental Aspects and In Vivo Applications

Next-generation neural interfaces for bidirectional communication with the central nervous system aim to achieve the intimate integration with the neural tissue with minimal neuroinflammatory response, high spatio-temporal resolution, very high sensitivity, and readout stability. The design and manufacturing of devices for low power/low noise neural recording and safe and energy-efficient stimulation that are, at the same time, conformable to the brain, with matched mechanical properties and biocompatibility, is a convergence area of research where neuroscientists, materials scientists, and nanotechnologists operate synergically. The biotic-abiotic neural interface, however, remains a formidable challenge that prompts for new materials platforms and innovation in device layouts. Conductive polymers (CP) are attractive materials to be interfaced with the neural tissue and to be used as sensing/stimulating electrodes because of their mixed ionic-electronic conductivity, their low contact impedance, high charge storage capacitance, chemical versatility, and biocompatibility. This manuscript reviews the state-of-the-art of poly(3,4-ethylenedioxythiophene)-based neural interfaces for extracellular recording and stimulation, focusing on those technological approaches that are successfully demonstrated in vivo. The aim is to highlight the most reliable and ready-for-clinical-use solutions, in terms of materials technology and recording performance, other than spot major limitations and identify future trends in this field.

Molecular Design Strategies toward Improvement of Charge Injection and Ionic Conduction in Organic Mixed Ionic-Electronic Conductors for Organic Electrochemical Transistors

Expanding the toolbox of the biology and electronics mutual conjunction is a primary aim of bioelectronics. The organic electrochemical transistor (OECT) has undeniably become a predominant device for mixed conduction materials, offering impressive transconduction properties alongside a relatively simple device architecture. In this review, we focus on the discussion of recent material developments in the area of mixed conductors for bioelectronic applications by means of thorough structure-property investigation and analysis of current challenges. Fundamental operation principles of the OECT are revisited, and characterization methods are highlighted. Current bioelectronic applications of organic mixed ionic-electronic conductors (OMIECs) are underlined. Challenges in the performance and operational stability of OECT channel materials as well as potential strategies for mitigating them, are discussed. This is further expanded to sketch a synopsis of the history of mixed conduction materials for both p- and n-type channel operation, detailing the synthetic challenges and milestones which have been overcome to frequently produce higher performing OECT devices. The cumulative work of multiple research groups is summarized, and synthetic design strategies are extracted to present a series of design principles that can be utilized to drive figure-of-merit performance values even further for future OMIEC materials.

Organic electrochemical neurons and synapses with ion mediated spiking

Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and operating principles fundamentally different from the ion signal modulation of biology, traditional Silicon-based neuromorphic implementations have limited bio-integration potential. Here, we report the first organic electrochemical neurons (OECNs) with ion-modulated spiking, based on all-printed complementary organic electrochemical transistors. We demonstrate facile bio-integration of OECNs with Venus Flytrap (Dionaea muscipula) to induce lobe closure upon input stimuli. The OECNs can also be integrated with all-printed organic electrochemical synapses (OECSs), exhibiting short-term plasticity with paired-pulse facilitation and long-term plasticity with retention >1000 s, facilitating Hebbian learning. These soft and flexible OECNs operate below 0.6 V and respond to multiple stimuli, defining a new vista for localized artificial neuronal systems possible to integrate with bio-signaling systems of plants, invertebrates, and vertebrates.

The effect of side chain engineering on conjugated polymers in organic electrochemical transistors for bioelectronic applications

Bioelectronics focuses on the establishment of the connection between the ion-driven biosystems and readable electronic signals. Organic electrochemical transistors (OECTs) offer a viable solution for this task. Organic mixed ionic/electronic conductors (OMIECs) rest at the heart of OECTs. The balance between the ionic and electronic conductivities of OMIECs is closely connected to the OECT device performance. While modification of the OMIECs' electronic properties is largely related to the development of conjugated scaffolds, properties such as ion permeability, solubility, flexibility, morphology, and sensitivity can be altered by side chain moieties. In this review, we uncover the influence of side chain molecular design on the properties and performance of OECTs. We summarise current understanding of OECT performance and focus specifically on the knowledge of ionic-electronic coupling, shedding light on the significance of side chain development of OMIECs. We show how the versatile synthetic toolbox of side chains can be successfully employed to tune OECT parameters via controlling the material properties. As the field continues to mature, more detailed investigations into the crucial role side chain engineering plays on the resultant OMIEC properties will allow for side chain alternatives to be developed and will ultimately lead to further enhancements within the field of OECT channel materials.

Radical Polymer-Based Organic Electrochemical Transistors

Organic electrochemical transistors (OECTs) are an emerging platform for bioelectronic applications. Significant effort has been placed in designing advanced polymers that simultaneously transport both charge and ions (i.e., macromolecules that are mixed conductors). However, the considerations for mixed organic conductors are often different from the established principles that are well-known in the solid-state organic electronics field; thus, the discovery of new OECT macromolecular systems is highly desired. Here, we demonstrate a new materials system by blending a radical polymer (i.e., a macromolecule with a nonconjugated backbone and with stable open-shell sites at its pendant group) with a frequently used conjugated polymer. Specifically, poly(4-glycidyloxy-2,2,6,6-tetramethylpiperidine-1-oxyl) (PTEO) was blended with poly(3-hexylthiophene) (P3HT) to create thin films with distinct closed-shell and open-shell domains. Importantly, the sharp and unique oxidation-reduction (redox) potential associated with the radical moieties of the PTEO chain provided a distinct actuation feature to the blended films that modulated the ionic transport of the OECT devices. In turn, this led to controlled regulation of the doping of the P3HT phase in the composite film. By decoupling the ionic and electronic transport into two distinct phases and by using an ion transport phase with well-controlled redox activity, never-before-seen performance for a P3HT-based OECT was observed. That is, at loadings as low as 5% PTEO (by weight) OECTs achieved figure-of-merit (i.e., μC*) values >150 F V^-1 cm^-1 s^-1, which place the performance on the same order as state-of-the-art conjugated polymers despite the relatively common conjugated macromolecular moiety implemented. As such, this effort presents a design platform by which to readily create a tailored OECT response through strategic macromolecular selection and polymer processing.

Influence of Molecular Weight on the Organic Electrochemical Transistor Performance of Ladder-Type Conjugated Polymers

Organic electrochemical transistors (OECTs) hold promise for developing a variety of high-performance (bio-)electronic devices/circuits. While OECTs based on p-type semiconductors have achieved tremendous progress in recent years, n-type OECTs still suffer from low performance, hampering the development of power-efficient electronics. Here, it is demonstrated that fine-tuning the molecular weight of the rigid, ladder-type n-type polymer poly(benzimidazobenzophenanthroline) (BBL) by only one order of magnitude (from 4.9 to 51 kDa) enables the development of n-type OECTs with record-high geometry-normalized transconductance (g_m,norm  ≈ 11 S cm^-1 ) and electron mobility × volumetric capacitance (µC* ≈ 26 F cm^-1  V^-1 s^-1 ), fast temporal response (0.38 ms), and low threshold voltage (0.15 V). This enhancement in OECT performance is ascribed to a more efficient intermolecular charge transport in high-molecular-weight BBL than in the low-molecular-weight counterpart. OECT-based complementary inverters are also demonstrated with record-high voltage gains of up to 100 V V^-1 and ultralow power consumption down to 0.32 nW, depending on the supply voltage. These devices are among the best sub-1 V complementary inverters reported to date. These findings demonstrate the importance of molecular weight in optimizing the OECT performance of rigid organic mixed ionic-electronic conductors and open for a new generation of power-efficient organic (bio-)electronic devices.

Dendritic Organic Electrochemical Transistors Grown by Electropolymerization for 3D Neuromorphic Engineering

One of the major limitations of standard top-down technologies used in today's neuromorphic engineering is their inability to map the 3D nature of biological brains. Here, it is shown how bipolar electropolymerization can be used to engineer 3D networks of PEDOT:PSS dendritic fibers. By controlling the growth conditions of the electropolymerized material, it is investigated how dendritic fibers can reproduce structural plasticity by creating structures of controllable shape. Gradual topologies evolution is demonstrated in a multielectrode configuration. A detailed electrical characterization of the PEDOT:PSS dendrites is conducted through DC and impedance spectroscopy measurements and it is shown how organic electrochemical transistors (OECT) can be realized with these structures. These measurements reveal that quasi-static and transient response of OECTs can be adjusted by controlling dendrites' morphologies. The unique properties of organic dendrites are used to demonstrate short-term, long-term, and structural plasticity, which are essential features required for future neuromorphic hardware development.

Analog programing of conducting-polymer dendritic interconnections and control of their morphology

Although materials and processes are different from biological cells', brain mimicries led to tremendous achievements in parallel information processing via neuromorphic engineering. Inexistent in electronics, we emulate dendritic morphogenesis by electropolymerization in water, aiming in operando material modification for hardware learning. Systematic study of applied voltage-pulse parameters details on tuning independently morphological aspects of micrometric dendrites': fractal number, branching degree, asymmetry, density or length. Growths time-lapse image processing shows spatial features to be dynamically dependent, and expand distinctively before and after conductive bridging with two electro-generated dendrites. Circuit-element analysis and impedance spectroscopy confirms their morphological control in temporal windows where growth kinetics is finely perturbed by the input frequency and duty cycle. By the emulation of one's most preponderant mechanisms for brain's long-term memory, its implementation in vicinity of sensing arrays, neural probes or biochips shall greatly optimize computational costs and recognition required to classify high-dimensional patterns from complex environments.

Flexible artificial Si-In-Zn-O/ion gel synapse and its application to sensory-neuromorphic system for sign language translation

We propose a flexible artificial synapse based on a silicon-indium-zinc-oxide (SIZO)/ion gel hybrid structure directly fabricated on a polyimide substrate, where the channel conductance is effectively modulated via ion movement in the ion gel. This synaptic operation is possible because of the low-temperature deposition process of the SIZO layer (<150°C) and the surface roughness improvement of the poly(4-vinylphenol) buffer layer (12.29→1.81 nm). The flexible synaptic device exhibits extremely stable synaptic performance under high mechanical (bending 1500 times with a radius of 5 mm) and electrical stress (application of voltage pulses 10⁴ times) without any degradation. Last, a sensory-neuromorphic system for sign language translation, which consists of stretchable resistive sensors and flexible artificial synapses, is designed and successfully evaluated via training and recognition simulation using hand sign patterns obtained by stretchable sensors (maximum recognition rate, 99.4%).

Organic electrochemical transistors in bioelectronic circuits

The organic electrochemical transistor (OECT) represents a versatile and impactful electronic building block in the areas of printed electronics, bioelectronics, and neuromorphic computing. Significant efforts in OECTs have focused on device physics, new active material design and synthesis, and on preliminary implementation of individual transistors as proof-of-concept components for sensing and computation. However, as most of the current studies are based on single devices, the integration of OECTs into circuits or high-level systems has lagged. In this review, we focus on recent efforts to incorporate individual OECTs into digital, analog, and neuromorphic circuits, and lay out important considerations relevant for (hybrid) systems integration. We summarize the operation principles and the functions of OECT-based circuits and discuss the approaches for wireless power and data transmission for practicality in biological and bio-inspired applications. Finally, we comment on the future directions and challenges facing OECT circuits from both a fundamental and applied perspective.

Electrolyte-gated transistors for enhanced performance bioelectronics

Electrolyte-gated transistors (EGTs), capable of transducing biological and biochemical inputs into amplified electronic signals and stably operating in aqueous environments, have emerged as fundamental building blocks in bioelectronics. In this Primer, the different EGT architectures are described with the fundamental mechanisms underpinning their functional operation, providing insight into key experiments including necessary data analysis and validation. Several organic and inorganic materials used in the EGT structures and the different fabrication approaches for an optimal experimental design are presented and compared. The functional bio-layers and/or biosystems integrated into or interfaced to EGTs, including self-organization and self-assembly strategies, are reviewed. Relevant and promising applications are discussed, including two-dimensional and three-dimensional cell monitoring, ultra-sensitive biosensors, electrophysiology, synaptic and neuromorphic bio-interfaces, prosthetics and robotics. Advantages, limitations and possible optimizations are also surveyed. Finally, current issues and future directions for further developments and applications are discussed.

Influence of Backbone Curvature on the Organic Electrochemical Transistor Performance of Glycolated Donor-Acceptor Conjugated Polymers

Two new glycolated semiconducting polymers PgBT(F)2gT and PgBT(F)2gTT of differing backbone curvatures were designed and synthesised for application as p-type accumulation mode organic electrochemical transistor (OECT) materials. Both polymers demonstrated stable and reversible oxidation, accessible within the aqueous electrochemical window, to generate polaronic charge carriers. OECTs fabricated from PgBT(F)2gT featuring a curved backbone geometry attained a higher volumetric capacitance of 170 F cm-3 . However, PgBT(F)2gTT with a linear backbone displayed overall superior OECT performance with a normalised peak transconductance of 3.00×104  mS cm-1 , owing to its enhanced order, expediting the charge mobility to 0.931 cm2  V-1  s-1 .

A non-printed integrated-circuit textile for wireless theranostics

While the printed circuit board (PCB) has been widely considered as the building block of integrated electronics, the world is switching to pursue new ways of merging integrated electronic circuits with textiles to create flexible and wearable devices. Herein, as an alternative for PCB, we described a non-printed integrated-circuit textile (NIT) for biomedical and theranostic application via a weaving method. All the devices are built as fibers or interlaced nodes and woven into a deformable textile integrated circuit. Built on an electrochemical gating principle, the fiber-woven-type transistors exhibit superior bending or stretching robustness, and were woven as a textile logical computing module to distinguish different emergencies. A fiber-type sweat sensor was woven with strain and light sensors fibers for simultaneously monitoring body health and the environment. With a photo-rechargeable energy textile based on a detailed power consumption analysis, the woven circuit textile is completely self-powered and capable of both wireless biomedical monitoring and early warning. The NIT could be used as a 24/7 private AI "nurse" for routine healthcare, diabetes monitoring, or emergencies such as hypoglycemia, metabolic alkalosis, and even COVID-19 patient care, a potential future on-body AI hardware and possibly a forerunner to fabric-like computers.

Nanofiber Channel Organic Electrochemical Transistors for Low-Power Neuromorphic Computing and Wide-Bandwidth Sensing Platforms

Organic neuromorphic computing/sensing platforms are a promising concept for local monitoring and processing of biological signals in real time. Neuromorphic devices and sensors with low conductance for low power consumption and high conductance for low-impedance sensing are desired. However, it has been a struggle to find materials and fabrication methods that satisfy both of these properties simultaneously in a single substrate. Here, nanofiber channels with a self-formed ion-blocking layer are fabricated to create organic electrochemical transistors (OECTs) that can be tailored to achieve low-power neuromorphic computing and fast-response sensing by transferring different amounts of electrospun nanofibers to each device. With their nanofiber architecture, the OECTs exhibit a low switching energy of 113 fJ and operate within a wide bandwidth (cut-off frequency of 13.5 kHz), opening a new paradigm for energy-efficient neuromorphic computing/sensing platforms in a biological environment without the leakage of personal information.

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Associative learning, a critical learning principle to improve an individual's adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.

Porous Semiconducting Polymers Enable High-Performance Electrochemical Transistors

Organic polymer electrochemical transistors (OECTs) are of great interest for flexible electronics and bioelectronics applications owing to their high transconductance and low operating voltage. However, efficient OECT operation must delicately balance the seemingly incompatible materials optimizations of redox chemistry, active layer electronic transport, and ion penetration/transport. The latter characteristics are particularly challenging since most high-mobility semiconducting polymers are hydrophobic, which hinders efficient ion penetration, hence limiting OECT performance. Here, the properties and OECT response of a series of dense and porous semiconducting polymer films are compared, the latter fabricated via a facile breath figure approach. This methodology enables fast ion doping, high transconductance (up to 364 S cm^-1 ), and a low subthreshold swing for the hydrophobic polymers DPPDTT and P3HT, rivalling or exceeding the metrics of the relatively hydrophilic polymer, Pg2T-T. Furthermore, the porous morphology also enhances the transconductance of hydrophilic polymers, offering a general strategy for fabricating high-performance electrochemical transistors.