Solution-Processable Anion-doped Conjugated Polymer for Nonvolatile Organic Transistor Memory with Synaptic Behaviors

Computational Investigation of the Potential and Limitations of Machine Learning with Neural Network Circuits Based on Synaptic Transistors

Synaptic transistors have been proposed to implement neuron activation functions of neural networks (NNs). While promising to enable compact, fast, inexpensive, and energy-efficient dedicated NN circuits, they also have limitations compared to digital NNs (realized as codes for digital processors), including shape choices of the activation function using particular types of transistor implementation, and instabilities due to noise and other factors present in analog circuits. We present a computational study of the effects of these factors on NN performance and find that, while accuracy competitive with traditional NNs can be realized for many applications, there is high sensitivity to the instability in the shape of the activation function, suggesting that, when highly accurate NNs are required, high-precision circuitry should be developed beyond what has been reported for synaptic transistors to date.

Graphene oxide-DNA/graphene oxide-PDDA sandwiched membranes with neuromorphic function

The behavior of polyelectrolytes in confined spaces has direct relevance to the protein mediated ion transport in living organisms. In this paper, we govern lithium chloride transport by the interface provided by polyelectrolytes, polycation, poly(diallyldimethylammonium chloride) (PDDA) and, polyanion, double stranded deoxyribonucleic acid (dsDNA), in confined graphene oxide (GO) membranes. Polyelectrolyte-GO interfaces demonstrate neuromorphic functions that were successfully applied with nanochannel ion interactions contributed, resulting in ion memory effects. Excitatory and inhibitory post-synaptic currents were tuned continuously as the number of pulses applied increased accordingly, increasing decay times. Furthermore, we demonstrated the short-term memory of a trained vs untrained device in computation. On account of its simple and safe production along with its robustness and stability, we anticipate our device to be a low dimensional building block for arrays to embed artificial neural networks in hardware for neuromorphic computing. Additionally, incorporating such devices with sensing and actuating parts for a complete feedback loop produces robotics with its own ability to learn by modifying actuation based on sensing data.

Recent advancements in implantable neural links based on organic synaptic transistors

The progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed.

Unveiling the Role of Side Chain for Improving Nonvolatile Characteristics of Conjugated Polymers-Based Artificial Synapse

Interest has grown in services that consume a significant amount of energy, such as large language models (LLMs), and research is being conducted worldwide on synaptic devices for neuromorphic hardware. However, various complex processes are problematic for the implementation of synaptic properties. Here, synaptic characteristics are implemented through a novel method, namely side chain control of conjugated polymers. The developed devices exhibit the characteristics of the biological brain, especially spike-timing-dependent plasticity (STDP), high-pass filtering, and long-term potentiation/depression (LTP/D). Moreover, the fabricated synaptic devices show enhanced nonvolatile characteristics, such as long retention time (≈102 s), high ratio of Gmax/Gmin, high linearity, and reliable cyclic endurance (≈103 pulses). This study presents a new pathway for next-generation neuromorphic computing by modulating conjugated polymers with side chain control, thereby achieving high-performance synaptic properties.

Dual Light Temporal Coding Modes Enabled by Nanoparticle-Mediated Phototransistors via Gate Bias Modulation for Brain-Inspired Visual Perception

The core integration and cooperation of the retina, neurons, and synapses in the visual systems enable humans to effectively sense and process visual information with low power consumption. To mimic the human visual system, an artificial sensory nerve, along with optical sensing─a paired-pulse ratio (PPR) of the light pulse stimulated currents─and neural coding has been developed. For performing the artificial visual perception functions, we consistently reveal the positive and negative correlations between the PPR index and light pulse time interval by applying two consecutive light stimuli with gate voltages of -10 and 5 V, respectively, to a phototransistor. This phototransistor contains a heterostructured channel layer composed of zinc-oxide nanoparticles (ZnO NPs) interconnected with a solution-processed zinc-tin oxide (ZTO) film. The oxygen adsorption and desorption on the ZnO NP surface under light illumination are responsible for the positive-sloped PPR; the electron trapping effect at the ZnO NP/SiO2 interface is attributed to the negative-sloped PPR. The various accountable light power densities and number of surface trap states are considered to be directly realizing these spike-timing interval-dependent characteristics. The actual benefit of these characteristics is the dual temporal coding modes based on multiplicative operation using a ZTO/ZnO NP phototransistor realized via the active gate voltage modulation. The contrary tendency of the PPR index and temporal coding─a major biological neural coding─is well demonstrated by the potential of ZTO/ZnO NP phototransistors to be implemented in sensor networks for an artificial visual perception.

Tuning Ambipolarity of the Conjugated Polymer Channel Layers of Floating-Gate Free Transistors: From Volatile Memories to Artificial Synapses

Three-terminal synaptic transistor has drawn significant research interests for neuromorphic computation due to its advantage of facile device integrability. Lately, bulk-heterojunction-based synaptic transistors with bipolar modulation are proposed to exempt the use of an additional floating gate. However, the actual correlation between the channel's ambipolarity, memory characteristic, and synaptic behavior for a floating-gate free transistor has not been investigated yet. Herein, by studying five diketopyrrolopyrrole-benzotriazole dual-acceptor random conjugated polymers, a clear correlation among the hole/electron ratio, the memory retention characteristic, and the synaptic behavior for the polymer channel layer in a floating-gate free transistor is described. It reveals that the polymers with balanced ambipolarity possess better charge trapping capabilities and larger memory windows; however, the high ambipolarity results in higher volatility of the memory characteristics, namely poor memory retention capability. In contrast, the polymer with a reduced ambipolarity possesses an enhanced memory retention capability despite showing a reduced memory window. It is further manifested that this enhanced charge retention capability enables the device to present artificial synaptic characteristics. The results highlight the importance of the channel's ambipolarity of floating-gate free transistors on the resultant volatile memory characteristics and synaptic behaviors.

Nanostructured perovskites for nonvolatile memory devices

Perovskite materials have driven tremendous advances in constructing electronic devices owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties, flexible dimensionality engineering, and so on. Particularly, emerging nonvolatile memory devices (eNVMs) based on perovskites give birth to numerous traditional paradigm terminators in the fields of storage and computation. Despite significant exploration efforts being devoted to perovskite-based high-density storage and neuromorphic electronic devices, research studies on materials' dimensionality that has dominant effects on perovskite electronics' performances are paid little attention; therefore, a review from the point of view of structural morphologies of perovskites is essential for constructing perovskite-based devices. Here, recent advances of perovskite-based eNVMs (memristors and field-effect-transistors) are reviewed in terms of the dimensionality of perovskite materials and their potentialities in storage or neuromorphic computing. The corresponding material preparation methods, device structures, working mechanisms, and unique features are showcased and evaluated in detail. Furthermore, a broad spectrum of advanced technologies (e.g., hardware-based neural networks, in-sensor computing, logic operation, physical unclonable functions, and true random number generator), which are successfully achieved for perovskite-based electronics, are investigated. It is obvious that this review will provide benchmarks for designing high-quality perovskite-based electronics for application in storage, neuromorphic computing, artificial intelligence, information security, etc.

Solution-Processed Perovskite Field-Effect Transistor Artificial Synapses

Metal halide perovskites are distinctive semiconductors that exhibit both ionic and electronic transport and are promising for artificial synapses. However, developing a 3-terminal transistor artificial synapse with the perovskite channel remains elusive due to the lack of a proper technique to regulate mobile ions in a non-volatile manner. Here, a solution-processed perovskite transistor is reported for artificial synapses through the implementation of a ferroelectric gate. The ferroelectric polarization provides a non-volatile electric field on the perovskite, leading to fixation of the mobile ions and hence modulation of the electronic conductance of the channel. Multi-state channel conductance is realized by partial ferroelectric polarization. The ferroelectric-gated perovskite transistor is successfully used as an artificial synapse that emulates basic synaptic functions such as long-term plasticity with excellent linearity, short-term as well as spike-timing-dependent plasticity. The strategy to regulate ion dynamics in the perovskites using the ferroelectric gate suggests a generic route to employ perovskites for synaptic electronics.

Doping Approaches for Organic Semiconductors

Electronic doping in organic materials has remained an elusive concept for several decades. It drew considerable attention in the early days in the quest for organic materials with high electrical conductivity, paving the way for the pioneering work on pristine organic semiconductors (OSCs) and their eventual use in a plethora of applications. Despite this early trend, however, recent strides in the field of organic electronics have been made hand in hand with the development and use of dopants to the point that are now ubiquitous. Here, we give an overview of all important advances in the area of doping of organic semiconductors and their applications. We first review the relevant literature with particular focus on the physical processes involved, discussing established mechanisms but also newly proposed theories. We then continue with a comprehensive summary of the most widely studied dopants to date, placing particular emphasis on the chemical strategies toward the synthesis of molecules with improved functionality. The processing routes toward doped organic films and the important doping-processing-nanostructure relationships, are also discussed. We conclude the review by highlighting how doping can enhance the operating characteristics of various organic devices.

Contribution of Polymers to Electronic Memory Devices and Applications

Electronic memory devices, such as memristors, charge trap memory, and floating-gate memory, have been developed over the last decade. The use of polymers in electronic memory devices enables new opportunities, including easy-to-fabricate processes, mechanical flexibility, and neuromorphic applications. This review revisits recent efforts on polymer-based electronic memory developments. The versatile contributions of polymers for emerging memory devices are classified, providing a timely overview of such unconventional functionalities with a strong emphasis on the merits of polymer utilization. Furthermore, this review discusses the opportunities and challenges of polymer-based memory devices with respect to their device performance and stability for practical applications.

Flexible Artificial Synapses with a Biocompatible Maltose-Ascorbic Acid Electrolyte Gate for Neuromorphic Computing

As constructing hardware technology is widely regarded as an important step toward realizing brain-like computers and artificial intelligence systems, the development of artificial synaptic electronics that can simulate biological synaptic functions is an emerging research field. Among the various types of artificial synapses, synaptic transistors using an electrolyte as the gate electrode have been implemented as the high capacitance of the electrolyte increases the driving current and lowers operating voltages. Here, transistors using maltose-ascorbic acid as the proton-conducting electrolyte are proposed. A novel electrolyte composed of maltose and ascorbic acid, both of which are biocompatible, enables the migration of protons. This allows the channel conductance of the transistors to be modulated with the gate input pulse voltage, and fundamental synaptic functions including excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation, and long-term depression can be successfully emulated. Furthermore, the maltose-ascorbic acid electrolyte (MAE)-gated synaptic transistors exhibit high mechanical endurance, with near-linear conductivity modulation and repeatability after 1000 bending cycles under a curvature radius of 5 mm. Benefitting from its excellent biodegradability and biocompatibility, the proposed MAE has potential applications in environmentally friendly, economical, and high-performance neuromorphic electronics, which can be further applied to dermal electronics and implantable electronics in the future.

Ecofriendly Solution-Combustion-Processed Thin-Film Transistors for Synaptic Emulation and Neuromorphic Computing

The ecofriendly combustion synthesis (ECS) and self-combustion synthesis (ESCS) have been successfully utilized to deposit high-k aluminum oxide (AlO_x) dielectrics at low temperatures and applied for aqueous In₂O₃ thin-film transistors (TFTs) accordingly. The ECS and ESCS processes facilitate the formation of high-quality dielectrics at lower temperatures compared to conventional methods based on an ethanol precursor, as confirmed by thermal analysis and chemical composition characterization. The aqueous In₂O₃ TFTs based on ECS and ESCS-AlO_x show enhanced electrical characteristics and counterclockwise transfer-curve hysteresis. The memory-like counterclockwise behavior in the transfer curve modulated by the gate bias voltage is comparable to the signal modulation by the neurotransmitters. ECS and ESCS transistors are employed to perform synaptic emulation; various short-term and long-term memory functions are emulated with low operating voltages and high excitatory postsynaptic current levels. High stability and reproducibility are achieved within 240 pulses of long-term synaptic potentiation and depression. The synaptic emulation functions achieved in this work match the demand for artificial neural networks (ANN), and a multilayer perceptron (MLP) is developed using an ECS-AlO_x synaptic transistor for image recognition. A superior recognition rate of over 90% is achieved based on ECS-AlO_x synaptic transistors, which facilitates the implementation of the metal-oxide synaptic transistor for future neuromorphic computing via an ecofriendly route.