Organic Memristor-Based Flexible Neural Networks with Bio-Realistic Synaptic Plasticity for Complex Combinatorial Optimization.

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems with complete synaptic plasticity for combinatorial optimization remains challenging. In this study, the metal-ion injection density is explored as a diffusive parameter of the conductive filament in organic memristors. Additionally, a flexible artificial synapse with bio-realistic synaptic plasticity is developed using organic memristors that have systematically engineered metal-ion injections, for the first time. In the proposed artificial synapse, short-term plasticity (STP), long-term plasticity, and homeostatic plasticity are independently achieved and are analogous to their biological counterparts. The time windows of the STP and homeostatic plasticity are controlled by the ion-injection density and electric-signal conditions, respectively. Moreover, stable capabilities for complex combinatorial optimization in the developed synapse arrays are demonstrated under spike-dependent operations. This effective concept for realizing flexible neuromorphic systems for complex combinatorial optimization is an essential building block for achieving a new paradigm of wearable smart electronics associated with artificial intelligent systems.

Enhancement of Charge Injection in Organic Field-Effect Transistors Through Semiconducting Organic Buffer Layer.

We investigate the effect of a semiconducting organic buffer layer (SOBL) on the injection and transport of charges in organic field-effect transistors (OFETs). Here, two different injection barriers at the source/organic semiconductor interface are respectively studied with the aid of a numerical simulation: one is intermediate (0.4 eV), and the other is large energy barriers (0.6 eV). The introduction of nanostructure buffer layer, or SOBL, exhibits the decrease of potential loss at the contact interfaces, improving the electrical performance of the OFETs. It is also found that the energy level as well as the mobility of the SOBL plays an important role in determining the injection properties at the metal/organic hetero-interfaces and thus improving the device performance. Our systematic investigation on the injection barrier by the introduction of the nanostructure buffer layer will provide a useful guideline for the fabrication of high-performance FETs with molecular semiconductors.

Reliable organic memristors for neuromorphic computing by predefining a localized ion-migration path in crosslinkable polymer.

In flexible neuromorphic systems for realizing artificial intelligence, organic memristors are essential building blocks as artificial synapses to perform information processing and memory. Despite much effort to implement artificial neural networks (ANNs) using organic memristors, the reliability of these devices is inherently hampered by global ion transportation and arbitrary growth of conductive filaments (CFs). As a result, the performance of ANNs is restricted. Herein, a novel concept for confining CF growth in organic memristors is demonstrated by exploiting the unique functionality of crosslinkable polymers. This can be achieved by predefining the localized ion-migration path (LIP) in crosslinkable polymers. In the proposed organic memristor, metal cations are locally transported along the LIP. Thus, CF growth is achieved only in a confined region. A flexible memristor with an LIP exhibits a vastly improved reliability and uniformity, and it is capable of operating with high mechanical and electrical endurance. Moreover, neuromorphic arrays based on the proposed memristor exhibit 96.3% learning accuracy, which is comparable to the ideal software baseline. The proposed concept of predefining the LIP in organic memristors is expected to provide novel platforms for the advance of flexible electronics and to realize a variety of practical neural networks for artificial intelligence applications.

Realization of Biomimetic Synaptic Functions in a One-Cell Organic Resistive Switching Device Using the Diffusive Parameter of Conductive Filaments.

Toward the successful development of artificial intelligence, artificial synapses based on resistive switching devices are essential ingredients to perform information processing in spiking neural networks. In neural processes, synaptic plasticity related to the history of neuron activity plays a critical role during learning. In resistive switching devices, it is barely possible to emulate both short-term plasticity and long-term plasticity due to the uncontrollable dynamics of the conductive filaments (CFs). Despite extensive effort to realize synaptic plasticity in such devices, it is still challenging to achieve reliable synaptic functions due to the overgrowth of CFs in a random fashion. Herein, we propose an organic resistive switching device with bio-realistic synaptic functions by adjusting the CF diffusive parameter. In the proposed device, complete synaptic plasticity provides the history-dependent change in the conductance. Moreover, the homeostatic feedback, which resembles the biological process, regulates CF growth in our device, which enhances the reliability of synaptic plasticity. This novel concept for realizing synaptic functions in organic resistive switching devices may provide a physical platform to advance the fundamental understanding of learning and memory mechanisms and develop a variety of neural circuits and neuromorphic systems that can be linked to artificial intelligence and next-generation computing paradigm.

Improvement of Photoresponse in Organic Phototransistors through Bulk Effect of Photoresponsive Gate Insulators.

In this study, we investigate the bulk effect of photoresponsive gate insulators on the photoresponse of organic phototransistors (OPTs), using OPTs with poly(4-vinylphenol) layers of two different thicknesses. For the photoresponse, the interplay between the charge accumulation (capacitance) and light-absorbance capabilities of a photoresponsive gate insulator was investigated. Although an OPT with a thicker gate insulator exhibits a lower capacitance and hence a lower accumulation capability of photogenerating charges, a thicker poly(4-vinylphenol) layer, in contrast to a thinner one, absorbs more photons to generate more electron-hole pairs, resulting in a higher photoresponse of the device. That is, in these two cases, the degree of light absorption by the photoresponsive gate insulators dominantly governed the photoresponse of the device. Our physical description of the bulk effect of photoresponsive insulators on the performance of OPTs will provide a useful guideline for designing and constructing high-performance organic-based photosensing devices and systems.

Enhanced Optical Switching Characteristics of Organic Phototransistor by Adopting Photo-Responsive Polymer in Hybrid Gate-Insulator Configuration.

In this study, we developed polymer gate insulator-based organic phototransistors (p-OPTs) with improved optical switching properties by using a hybrid gate insulator configuration. The hybrid gate insulator of our p-OPT has a photoresponsive layer made of poly(4-vinylphenol) (PVP), which enhances the photoresponse, and an interfacial layer of poly(methyl methacrylate) for reliable optical switching of the device. Our hybrid gate insulator-equipped p-OPT exhibits well-defined optical switching characteristics because no specific type of charge is significantly trapped at an interfacial layer/organic semiconductor (OSC) interface. Moreover, our device is more photoresponsive than the conventional p-OPT (here, an OPT with a single-polymer poly(methyl methacrylate) (PMMA) gate insulator), because the characteristic ultraviolet (UV) absorption of the PVP polymer allows the photoresponsive layer and OSC to contribute to the generation of charge carriers when exposed to UV light.

Retina-Inspired Carbon Nitride-Based Photonic Synapses for Selective Detection of UV Light.

Photonic synapses combine sensing and processing in a single device, so they are promising candidates to emulate visual perception of a biological retina. However, photonic synapses with wavelength selectivity, which is a key property for visual perception, have not been developed so far. Herein, organic photonic synapses that selectively detect UV rays and process various optical stimuli are presented. The photonic synapses use carbon nitride (C3 N4 ) as an UV-responsive floating-gate layer in transistor geometry. C3 N4 nanodots dominantly absorb UV light; this trait is the basis of UV selectivity in these photonic synapses. The presented devices consume only 18.06 fJ per synaptic event, which is comparable to the energy consumption of biological synapses. Furthermore, in situ modulation of exposure to UV light is demonstrated by integrating the devices with UV transmittance modulators. These smart systems can be further developed to combine detection and dose-calculation to determine how and when to decrease UV transmittance for preventive health care.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics.

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

Interfacial Triggering of Conductive Filament Growth in Organic Flexible Memristor for High Reliability and Uniformity.

We demonstrate the physical pictures of the localization of the conductive filaments (CFs) growth in flexible electrochemical metallization (ECM) memristors through an interfacial triggering (IT) into the polymer electrolyte. The IT sites (ITSs), capable of controlling the pathways of the CF growth, are formed at the electrode-polymer interfaces via the Ostwald ripening at low temperatures (below 230 °C). The injection and migration of metal ions and the resultant CF growth are found to be effectively controlled through the ITSs with the local electric field enhancement. The reliability, uniformity, and switching voltage of the device are much improved by the presence of the ITSs. Our flexible ECM memristor exhibits a high mechanical flexibility and a stable memory performance under repeated bending deformations.

Array of solid-state dye-sensitized solar cells with micropatterned TiO2 nanoparticles for a high-voltage power source.

We demonstrate an array of solid-state dye-sensitized solar cells (SS-DSSCs) for a high-voltage power source based on micropatterned titanium dioxide nanoparticles (TNPs) as photoanodes connected in series. The underlying concept of patterning the TNP of a few micrometers thick lies on the combination of the lift-off process of transfer-printed patterns of a sacrificial layer and the soft-cure treatment of the TNP for fixation. This sacrificial layer approach allows for high pattern fidelity and stability, and it enables to construct stable, micrometer-thick, and contamination-free TNP patterns for developing the SS-DSSC array for miniature high-voltage applications. The array of 20 SS-DSSCs integrated in series is found to show a voltage output of around 7 V.