Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFlexible Metal Oxide/Graphene Oxide Hybrid Neuromorphic Devices on Flexible Conducting Graphene Substrates
97
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Flexible metal oxide/graphene oxide hybrid multi-gate neuron transistors were fabricated on flexible graphene substrates. Dendritic integrations in both spatial and temporal modes were successfully emulated, and spatiotemporal correlated logics were obtained. A proof-of-principle visual system model for emulating lobula giant motion detector neuron was investigated. Our results are of great interest for flexible neuromorphic cognitive systems.
rate research
Read More
We present results from a new approach to learning and plasticity in neuromorphic hardware systems: to enable flexibility in implementable learning mechanisms while keeping high efficiency associated with neuromorphic implementations, we combine a general-purpose processor with full-custom analog elements. This processor is operating in parallel with a fully parallel neuromorphic system consisting of an array of synapses connected to analog, continuous time neuron circuits. Novel analog correlation sensor circuits process spike events for each synapse in parallel and in real-time. The processor uses this pre-processing to compute new weights possibly using additional information following its program. Therefore, learning rules can be defined in software giving a large degree of flexibility. Synapses realize correlation detection geared towards Spike-Timing Dependent Plasticity (STDP) as central computational primitive in the analog domain. Operating at a speed-up factor of 1000 compared to biological time-scale, we measure time-constants from tens to hundreds of micro-seconds. We analyze variability across multiple chips and demonstrate learning using a multiplicative STDP rule. We conclude, that the presented approach will enable flexible and efficient learning as a platform for neuroscientific research and technological applications.
Neuron is the most important building block in our brain, and information processing in individual neuron involves the transformation of input synaptic spike trains into an appropriate output spike train. Hardware implementation of neuron by individual ionic/electronic hybrid device is of great significance for enhancing our understanding of the brain and solving sensory processing and complex recognition tasks. Here, we provide a proof-of-principle artificial neuron based on a proton conducting graphene oxide (GO) coupled oxide-based electric-double-layer (EDL) transistor with multiple driving inputs and one modulatory input terminal. Paired-pulse facilitation, dendritic integration and orientation tuning were successfully emulated. Additionally, neuronal gain control (arithmetic) in the scheme of rate coding is also experimentally demonstrated. Our results provide a new-concept approach for building brain-inspired cognitive systems.
Flexible energy harvesting devices fabricated in scalable thin-film processes are important components in the field of wearable electronics and the Internet of Things. We present a flexible rectenna based on a one-dimensional junction metal-insulator-graphene diode, which offers low-noise power detection at terahertz (THz) frequencies. The rectennas are fabricated on a flexible polyimide film in a scalable process by photolithography using graphene grown by chemical vapor deposition. A one-dimensional junction area reduces the junction capacitance and enables operation in the D-band (110 - 170 GHz). The rectenna on polyimide shows a maximum voltage responsivity of 80 V/W at 167 GHz in free space measurements and minimum noise equivalent power of 80 pW/$sqrt{text{Hz}}$.
We report on the integration of large area CVD grown single- and bilayer graphene transparent conductive electrodes (TCEs) on amorphous silicon multispectral photodetectors. The broadband transmission of graphene results in 440% enhancement of the detectors spectral response in the ultraviolet (UV) region at {lambda} = 320 nm compared to reference devices with conventional aluminum doped zinc oxide (ZnO:Al) electrodes. The maximum responsivity of the multispectral photodetectors can be tuned in their wavelength from 320 nm to 510 nm by an external bias voltage, allowing single pixel detection of UV to visible light. Graphene electrodes further enable fully flexible diodes on polyimide substrates. Here, an upgrade from single to bilayer graphene boosts the maximum photoresponsivity from 134 mA $W^{-1}$ to 239 mA $W^{-1}$. Interference patterns that are present in conventional TCE devices are suppressed as a result of the atomically thin graphene electrodes. The proposed detectors may be of interest in fields of UV/VIS spectroscopy or for biomedical and life science applications, where the extension to the UV range can be essential.
Brain-inspired neuromorphic computing which consist neurons and synapses, with an ability to perform complex information processing has unfolded a new paradigm of computing to overcome the von Neumann bottleneck. Electronic synaptic memristor devices which can compete with the biological synapses are indeed significant for neuromorphic computing. In this work, we demonstrate our efforts to develop and realize the graphene oxide (GO) based memristor device as a synaptic device, which mimic as a biological synapse. Indeed, this device exhibits the essential synaptic learning behavior including analog memory characteristics, potentiation and depression. Furthermore, spike-timing-dependent-plasticity learning rule is mimicked by engineering the pre- and post-synaptic spikes. In addition, non-volatile properties such as endurance, retentivity, multilevel switching of the device are explored. These results suggest that Ag/GO/FTO memristor device would indeed be a potential candidate for future neuromorphic computing applications. Keywords: RRAM, Graphene oxide, neuromorphic computing, synaptic device, potentiation, depression
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
