Synaptic transistors and neuromorphic systems based on carbon nano-materials

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 Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.

Bio-inspired artificial synapse for neuromorphic computing based on NiO nanoparticle thin film

The unprecedented need for data processing in the modern technological era has created opportunities in neuromorphic devices and computation. This is primarily due to the extensive parallel processing done in our human brain. Data processing and logical decision-making at the same physical location are an exciting aspect of neuromorphic computation. For this, establishing reliable resistive switching devices working at room temperature with ease of fabrication is important. Here, a reliable analog resistive switching device based on Au/NiO nanoparticles/Au is discussed. The application of positive and negative voltage pulses of constant amplitude results in enhancement and reduction of synaptic current, which is consistent with potentiation and depression, respectively. The change in the conductance resulting in such a process can be fitted well with double exponential growth and decay, respectively. Consistent potentiation and depression characteristics reveal that non-ideal voltage pulses can result in a linear dependence of potentiation and depression. Long-term potentiation (LTP) and Long-term depression (LTD) characteristics have been established, which are essential for mimicking the biological synaptic applications. The NiO nanoparticle-based devices can also be used for controlled synaptic enhancement by optimizing the electric pulses, displaying typical learning-forgetting-relearning characteristics.

Environmentally Stable and Reconfigurable Ultralow-Power Two-Dimensional Tellurene Synaptic Transistor for Neuromorphic Edge Computing

While neuromorphic computing can define a new era for next-generation computing architecture, the introduction of an efficient synaptic transistor for neuromorphic edge computing still remains a challenge. Here, we envision an atomically thin 2D Te synaptic device capable of achieving a desirable neuromorphic edge computing design. The hydrothermally grown 2D Te nanosheet synaptic transistor apparently mimicked the biological synaptic nature, exhibiting 100 effective multilevel states, a low power consumption of ∼110 fJ, excellent linearity, and short-/long-term plasticity. Furthermore, the 2D Te synaptic device achieved reconfigurable MNIST recognition accuracy characteristics of 88.2%, even after harmful detergent environment infection. We believe that this work serves as a guide for developing futuristic neuromorphic edge computing.

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼10³) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.

Large area roll-to-roll printed semiconducting carbon nanotube thin films for flexible carbon-based electronics

A universal roll-to-roll (R2R) printing approach was developed to construct large area (8 cm × 14 cm) semiconducting single-walled carbon nanotube (sc-SWCNT) thin films on flexible substrates (such as polyethylene terephthalate (PET), paper, and Al foils) at a printing speed of 8 m min^-1 using highly concentrated sc-SWCNT inks and crosslinked poly-4-vinylphenol (c-PVP) as the adhesion layer. Bottom-gated and top-gated flexible printed p-type TFTs based on R2R printed sc-SWCNT thin films exhibited good electrical properties with a carrier mobility of ∼11.9 cm² V^-1 s^-1, I_on/I_off ratios of ∼10⁶, small hysteresis, and a subthreshold swing (SS) of 70-80 mV dec^-1 at low gate operating voltages (±1 V), and excellent mechanical flexibility. Furthermore, the flexible printed complementary metal oxide semiconductor (CMOS) inverters demonstrated rail-to-rail voltage output characteristics under an operating voltage as low as V_DD = -0.2 V, a voltage gain of 10.8 at V_DD = -0.8 V, and power consumption as low as 0.056 nW at V_DD = -0.2 V. To the best of our knowledge, the electrical properties of the printed SWCNT TFTs (such as I_on/I_off ratio, mobility, operating voltage, and mechanical flexibility) and printed CMOS inverters based on the R2R printed sc-SWCNT active layer in this work are excellent compared to those of R2R printed SWCNT TFTs reported in the literature. Consequently, the universal R2R printing method reported in this work could promote the development of fully printed low-cost, large-area, high-output, and flexible carbon-based electronics.

Biological function simulation in neuromorphic devices: from synapse and neuron to behavior

As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain's functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov's dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.

Spatially confined growth of carbon nanotubes in the pore channels of microporous ceramic supports with improved filtration efficiency

Carbon nanotubes (CNTs) with high degrees of uniformity, orientation and controlled dimensions on porous supports are highly desirable for various applications such as separation of O/W emulsions and air purification. In this work, CNTs were fabricated on silicon carbide (SiC) porous supports with different porosities and pore sizes by chemical vapor deposition (CVD). The growth processes of CNTs on the surface and in the pore channels of the SiC support were studied in detail. Based on microstructural characterization by SEM, Raman spectroscopy and TEM, it was found that these CNTs grown in the pore channels of SiC supports had a higher degree of orientation and purity than those grown on the surface due to the spatially confined effect. The growth processes of various types of CNTs on the microporous supports were proposed, which were further verified by CNTs with different steric configurations (S-CNTs and VACNTs) and on Al₂O₃ porous supports. Moreover, the contribution of CNTs in the pore channels to the filtration efficiency was demonstrated in oil-water emulsion separation and particle removal in air. This work provides significant guidance for the preparation and filtration application of CNTs on porous materials.

Emulation of Synaptic Plasticity on a Cobalt-Based Synaptic Transistor for Neuromorphic Computing

Neuromorphic computing (NC), which emulates neural activities of the human brain, is considered for the low-power implementation of artificial intelligence. Toward realizing NC, fabrication, and investigations of hardware elements─such as synaptic devices and neurons─are crucial. Electrolyte gating has been widely used for conductance modulation by massive carrier injections and has proven to be an effective way of emulating biological synapses. Synaptic devices, in the form of synaptic transistors, have been studied using various materials. Despite the remarkable progress, the study of metallic channel-based synaptic transistors remains massively unexplored. Here, we demonstrated a three-terminal electrolyte gating-modulated synaptic transistor based on a metallic cobalt thin film to emulate biological synapses. We have realized gating-controlled, non-volatile, and distinct multilevel conductance states in the proposed device. The essential synaptic functions demonstrating both short-term and long-term plasticity have been emulated in the synaptic device. A transition from short-term to long-term memory has been realized by tuning the gate pulse parameters, such as amplitude and duration. The crucial cognitive behavior, including learning, forgetting, and re-learning, has been emulated, showing a resemblance to the human brain. Beyond that, dynamic filtering behavior has been experimentally implemented in the synaptic device. These results provide an insight into the design of metallic channel-based synaptic transistors for NC.

Ultralow-power flexible transparent carbon nanotube synaptic transistors for emotional memory

Emulating the biological behavior of the human brain with artificial neuromorphic devices is essential for the future development of human-machine interactive systems, bionic sensing systems and intelligent robotic systems. In this paper, artificial flexible transparent carbon nanotube synaptic transistors (F-CNT-STs) with signal transmission and emotional learning functions are realized by adopting the poly(vinyl alcohol) (PVA)/SiO2 proton-conducting electrolyte. Synaptic functions of biological synapses including excitatory and inhibitory behaviors are successfully emulated in the F-CNT-STs. Besides, synaptic plasticity such as spike-duration-dependent plasticity, spike-number-dependent plasticity, spike-amplitude-dependent plasticity, paired-pulse facilitation, short-term plasticity, and long-term plasticity have all been systematically characterized. Moreover, the F-CNT-STs also closely imitate the behavior of human brain learning and emotional memory functions. After 1000 bending cycles at a radius of 3 mm, both the transistor characteristics and the synaptic functions can still be implemented correctly, showing outstanding mechanical capability. The realized F-CNT-STs possess low operating voltage, quick response, and ultra-low power consumption, indicating their high potential to work in low-power biological systems and artificial intelligence systems. The flexible artificial synaptic transistor enables its potential to be generally applicable to various flexible wearable biological and intelligent applications.