Organic Synapse Research Articles

Photogating-based organic synapse electronics modulated by dielectric

The greatest strength of photogating effect is that the current level can be adjusted by light. Research on photogating effect is still actively underway, and various factors play a role in causing photo-reaction. In general, they work in such a way that photo-generated carriers generated under certain conditions are trapped, leading to threshold voltage shifts. Here we focus on the surface environment in which photocarriers are trapped/de-trapped. To analyze the difference in photo-response depending on the insulating layer of the phototransistor, we produced a DNTT TFTs with a SiO2 dielectric and a DNTT TFTs with a TiO2/SiO2 dielectric. Except for the insulating layer, the channel and source/drain electrodes use dinaphtho [2,3-b:2′,3′- f]thieno [3,23,2-b]thiophene (DNTT) and Au. 500 nm and 600 nm irradiation allowed a gradual current rise while DNTT TFTs with a TiO2/SiO2 dielectric allowed fast de-trapping. Additionally, facial recognition synapse simulation using 500 nm and 600 nm light using a DNTT TFTs with a TiO2/SiO2 dielectric achieved recognition rates of over 90%.

A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication.

Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time-varied synaptic rule, an in-memory asymmetric encryption as pre-authentication is utilized with subsequent convolutional neural network (ConvNet) for target recognition, achieving in-memory two-factor authentication (IM-2FA). The unipolar self-oscillated synaptic behavior is adopted to function as in-memory asymmetric encryption, which can greatly decrease the complexity of the peripheral circuit compared to bipolar stimulation. Results show that without passing the encryption process with suitable weights at the correct time, the ConvNet for target recognition will not work properly with an extremely low accuracy lower than 0.86%, thus effectively blocking out the potential risks of involuntary access. When a set of correct weights is evolved at a suitable time, a recognition rate as high as 99.82% can be implemented for target recognition, which verifies the effectiveness of the IM-2FA strategy.

An ultrasmall organic synapse for neuromorphic computing

High‐performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain‐inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high‐density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as‐fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle‐to‐cycle uniformity of 98.89% and device‐to‐device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking‐plasticity‐related algorithm for decision-making tasks.

Adaptive Memory of a Neuromorphic Transistor with Multi-Sensory Signal Fusion.

One of the ultimate goals of artificial intelligence is to achieve the capability of memory evolution and adaptability to changing environments, which is termed adaptive memory. To realize adaptive memory in artificial neuromorphic devices, artificial synapses with multi-sensing capability are required to collect and analyze various sensory cues from the external changing environments. However, due to the lack of platforms for mediating multiple sensory signals, most artificial synapses have been mainly limited to unimodal or bimodal sensory devices. Herein, we present a multi-modal artificial sensory synapse (MASS) based on an organic synapse to realize sensory fusion and adaptive memory. The MASS receives optical, electrical, and pressure information and in turn generates typical synaptic behaviors, mimicking the multi-sensory neurons in the brain. Sophisticated synaptic behaviors, such as Pavlovian dogs, writing/erasing, signal accumulation, and offset, were emulated to demonstrate the joint efforts of bimodal sensory cues. Moreover, associative memory can be formed in the device and be subsequently reshaped by signals from a third perception, mimicking modification of the memory and cognition when encountering a new environment. Our MASS provides a step toward next-generation artificial neural networks with an adaptive memory capability.

Ferroelectrics‐Electret Synergetic Organic Artificial Synapses with Single‐Polarity Driven Dynamic Reconfigurable Modulation

AbstractMost current studies of artificial synapses only mimic the static plasticity, which is far from achieving the complex behaviors of the human brain. The few reported dynamic reconfigurable synapses based on ambipolar transistors switch the operating states by voltages with opposite polarity, which impedes the development of highly efficient synaptic readout circuits. To improve the efficiency, flexibility, and biocompatibility of dynamic reconfigurable synapses, here a ferroelectrics‐electret synergetic organic synaptic p‐type transistor (FESOST) is devised. Owing to the synergetic action of ferroelectric polarization switching and charge capture, FESOST exhibits single‐polarity driven dynamic reconfigurable operating states with different synaptic behaviors (potentiation and depression) in response to the same gate pulse in different modes (excitatory and inhibitory). In addition, various single‐polarity driven synaptic behaviors including short‐term/long‐term plasticity, paired‐pulse facilitation/depression, spike‐rate‐dependent plasticity, and spike‐number‐dependent plasticity are also simulated. Finally, the reconfigurable artificial temperature perception system is simulated for the complex emotions of humans in response to different weather stimuli for people of different constitutions. The novel device architecture represents a major step forward in the development of dynamic, reconfigurable, high‐efficiency, organic synapses.

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.

A flexible biohybrid reflex arc mimicking neurotransmitter transmission

The simulation of neurotransmitter transmission in the biological nervous system holds great potential for the next generation of implantable neurological devices. Two challenges are (1) the fabrication of synaptic devices with selectivity to neurotransmitters in artificial reflex arcs and (2) the direct construction of biocompatible interfaces between biological tissues and devices to form biofeedback. Herein, we report the construction of a flexible biohybrid artificial motor biofeedback inspired by neuromuscular transmission of acetylcholine (ACh). Biocompatible interfaces are built by seeding biological cells on the gate of the organic synapse. The ACh secreted from rat adrenal pheochromocytoma (PC-12) cells is converted by the organic synapse into electrical signals to modulate the excitatory postsynaptic currents (EPSCs) to drive the soft actuator. This reflex arc enables memory consolidation on exposure to ACh, exhibiting both short-term plasticity and specificity. This method may provide a way for nerve repair and development of neural prosthetics and facilitate human-machine interfaces. • The organic synaptic device exhibits ACh-mediated plasticity with high selectivity • PC-12 cells act as the presynaptic membrane in the artificial neuromuscular junction • The reflex arc converts biological signals into movement of the actuator A flexible biohybrid reflex arc system is constructed by Shao et al. to mimic biological neuromuscular information transmission. The device converts chemical signals of ACh secreted by PC-12 cells into electrical signals, and the resulting excitatory postsynaptic currents (EPSCs) of the organic synaptic transistor are amplified to drive the movement of artificial muscle.

Organic Artificial Synapse for Human-Friendly Wearable Neuromorphic Electronics

Organic Artificial Synapse for Human-Friendly Wearable Neuromorphic Electronics

Self-powered flexible artificial synapse for near-infrared light detection

In the era of artificial intelligence, photonic artificial synapses that are capable of light detection and information storage are attracting much attention, although complex device configuration and high energy consumption are two remaining challenges. Here, a flexible polymer-based optical synapse for infrared light detection in the absence of applied voltage is fabricated to emulate human synaptic photoplasticity. Investigation into the mechanism of the self-powered properties demonstrates that both the localized electric field of the polyethylene terephthalate (PET) substrate and the high dielectric constant of narrow-band conjugated polymer contribute to spontaneous exciton dissociation. Moreover, array imaging and data encryption are achieved by modulating light for the flexible devices, which exhibit excellent mechanical flexibility and long-term air stability. Thus, this work may provide a strategy to develop self-powered artificial synapses for infrared light detection. Short-wavelength infrared light-responsive organic synapse Self-powered flexible photonic synapse with two-terminal device structure Polymer with dielectric constant as high as 5.8 Visual memory array imaging and encryption model application for near-infrared light To mimic the human visual system, Chen et al. adopt organic materials to sense and memorize light, including infrared light. The simple device can operate without voltage, and the strategy may provide sustainable energy solutions for artificial intelligence applications.

Vertical organic synapse expandable to 3D crossbar array

Recently, three-terminal synaptic devices have attracted considerable attention owing to their nondestructive weight-update behavior, which is attributed to the completely separated terminals for reading and writing. However, the structural limitations of these devices, such as a low array density and complex line design, are predicted to result in low processing speeds and high energy consumption of the entire system. Here, we propose a vertical three-terminal synapse featuring a remote weight update via ion gel, which is also extendable to a crossbar array structure. This synaptic device exhibits excellent synaptic characteristics, which are achieved via precise control of ion penetration onto the vertical channel through the weight-control terminal. Especially, the applicability of the developed vertical organic synapse array to neuromorphic computing is demonstrated using a simple crossbar synapse array. The proposed synaptic device technology is expected to be an important steppingstone to the development of high-performance and high-density neural networks.

Rational Band Engineering of an Organic Double Heterojunction for Artificial Synaptic Devices with Enhanced State Retention and Linear Update of Synaptic Weight.

Herein, we propose an organic double heterojunction to enable a nonvolatile step modulation of the conductance of an artificial synapse; the double heterojunction is composed of N,N'-dioctyl-3,4,9,10-perylene tetracarboxylic diimide (PTCDI-C8), copper phthalocyanine (CuPc), and para-sexiphenyl (p-6P). The carrier confinement in the CuPc region present in the double-heterojunction structure enabled the nonvolatile modulation of the postsynaptic current. The proposed organic synapse exhibited an excellent conductance change, characteristic with a nonlinearity (NL) value below 0.01 in the long-term potentiation (LTP) region. Furthermore, the NL value for long-term depression (LTD) could be reduced effectively from 45 to 3.5 by a pulse modulation technique. A simple artificial neural network (ANN) was theoretically designed using the LTP/LTD characteristic curves of such organic synapses, and then, learning and recognition tasks were performed using Modified National Institute of Standards and Technology digit images. A four-amplitude weight update method enabled considerable enhancement of the recognition rate from 53 to 70%. Although the designed ANN was based on a single-layer perceptron model, a high maximum accuracy of 75% was achieved. These newly studied techniques for synaptic devices are expected to open up new possibilities for the realization of artificial synapses based on organic double heterojunctions.

Manipulating the hysteresis via dielectric in organic field-effect transistors toward synaptic applications

Abstract The cause of hysteresis in organic field-effect transistors (OFETs) and its application in organic synaptic transistors are studied. Benefiting from the slow polarization process of dipoles in organosilicone (Dow Corning 1–2577) dielectric, the devices exhibit the desirable synaptic behaviors, including excitatory postsynaptic current (EPSC), memory function, potentiation/depression characteristics, endurance characteristics and spike-amplitude-dependent plasticity. Further, combining the DC 1–2577 dielectric with the polydimethylsiloxane (PDMS) support, a flexible conformable DC 1–2577 dielectric organic synapse transistor is fabricated, which provides a great potential for the wearable and implantable artificial neuromorphic systems for artificial intelligence and new-generation computers.

A Ferroelectric/Electrochemical Modulated Organic Synapse for Ultraflexible, Artificial Visual‐Perception System

Human eyes undertake the majority of information assimilation for learning and memory. Transduction of the color and intensity of the incident light into neural signals is the main process for visual perception. Besides light-sensitive elements that function as rods and cones, artificial retinal systems require neuromorphic devices to transform light stimuli into post-synaptic signals. In terms of plasticity timescale, synapses with short-term plasticity (STP) and long-term potentiation (LTP) represent the neural foundation for experience acquisition and memory formation. Currently, electrochemical transistors are being researched as STP-LTP devices. However, their LTP timescale is confined to a second-to-minute level to give unreliable non-volatile memory. This issue limits multiple-plasticity synapses with tunable temporal characteristics and efficient sensory-memory systems. Herein, a ferroelectric/electrochemical modulated organic synapse is proposed, attaining three prototypes of plasticity: STP/LTP by electrochemical doping/de-doping and ferroelectric-LTP from dipole switching. The device supplements conventional electrochemical transistors with 10000-second-persistent non-volatile plasticity and unique threshold switching properties. As a proof-of-concept for an artificial visual-perception system, an ultraflexible, light-triggered organic neuromorphic device (LOND) is constructed by this synapse. The LOND transduces incident light signals with different frequency, intensity, and wavelength into synaptic signals, both volatile and non-volatile.

Electrolyte-gated organic synapse transistor interfaced with neurons

We demonstrate an electrolyte-gated hybrid nanoparticle/organic synapstor (synapse-transistor, termed EGOS) that exhibits short-term plasticity as biological synapses. The response of EGOS makes it suitable to be interfaced with neurons: short-term plasticity is observed at spike voltage as low as 50 mV (in a par with the amplitude of action potential in neurons) and with a typical response time in the range of tens milliseconds. Human neuroblastoma stem cells are adhered and differentiated into neurons on top of EGOS. We observe that the presence of the cells does not alter short-term plasticity of the device.