A method for direct growth of graphene nanowalls (GNWs) on an insulating substrate by plasma enhanced chemical vapor deposition (PECVD) is reported. The effects of growth temperature, plasma power, carbon source concentration, gas ratio and growth time on the quality of GNWs are systematically studied. The Raman spectrum shows that the obtained GNWs have a relatively high quality with a D to G peak ratio (ID/IG) of 0.42. Based on the optimization of the quality of GNWs, a field-effect transistor (FET) photodetector is prepared for the first time, and its photo-response mechanism is analyzed. The responsivity of the photodetector is 160 mA/W at 792 nm and 55 mA/W at 1550 nm. The results reveal that the GNWs are promising for high performance photodetectors.
Graphene is an ideal material for wide spectrum detector owing to its special band structure, but its low light absorption and fast composite of photogenerated carriers lead to a weak response performance. In this paper, we designed a unique photoconductive graphene-InGaAs photodetector. The built-in electric field was formed between graphene and InGaAs, which can prolong the lifetime of photogenerated carriers and improve the response of devices by confining the holes. Compared with graphene-Si structure, a higher built-in electric field and reach to 0.54 eV is formed. It enables the device to achieve a responsivity of 60 AW−1 and a photoconductive gain of 79.4 at 792 nm. In the 1550 nm communication band, the responsivity of the device is also greater than 10 AW−1 and response speed is less than 2 ms. Meanwhile, the saturation phenomenon of light response was also found in this photoconductive graphene heterojunction detector during the experiment, we have explained the phenomenon by the capacitance theory of the built-in electric field, and the maximum optical responsivity of the detector is calculated theoretically, which is in good agreement with the measurement result.
Artificial optoelectronic synapses have drawn tremendous attention in neuromorphic computing due to their exceptional properties of incorporating optical‐sensing and synaptic functions. However, the complex fabrication processes and device architectures greatly limit their applications. More importantly, artificial neural networks (ANNs) commonly implemented with optoelectronic synapses cannot take full advantage of the time‐dependent data of synaptic devices, resulting in defective accuracies. Here, facile two‐terminal optoelectronic synapses based on topological insulator Sb2Te3 films are fabricated, which exhibit significant photocurrent responses, owing to the efficient light‐matter interaction in bulk and the topological surface state of Sb2Te3. The performance of Sb2Te3 devices can be tuned both optically and electrically. Typical characteristics of synapses, such as paired‐pulse facilitation, short‐term memory, long‐term memory, and learning behavior, have been demonstrated. With the establishment of recurrent neural networks (RNNs) that are committed to processing temporal data, the as‐fabricated synapse devices are employed for binary image recognition of handwritten numbers “0” and “1”. The recognition accuracy of RNNs can reach as high as 100%, which is dramatically higher than those of ANNs. The effective employment of temporal data with RNNs ensured high recognition accuracy. These Sb2Te3 optoelectronic synapses with RNNs indicate the great potential for developing high‐performance brain‐inspired neuromorphic computing.
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