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A fast and low-power microelectromechanical system-based non-volatile memory device.

ABSTRACT: Several new generation memory devices have been developed to overcome the low performance of conventional silicon-based flash memory. In this study, we demonstrate a novel non-volatile memory design based on the electromechanical motion of a cantilever to provide fast charging and discharging of a floating-gate electrode. The operation is demonstrated by using an electromechanical metal cantilever to charge a floating gate that controls the charge transport through a carbon nanotube field-effect transistor. The set and reset currents are unchanged after more than 11 h constant operation. Over 500 repeated programming and erasing cycles were demonstrated under atmospheric conditions at room temperature without degradation. Multinary bit programming can be achieved by varying the voltage on the cantilever. The operation speed of the device is faster than a conventional flash memory and the power consumption is lower than other memory devices.

SUBMITTER: Lee SW

PROVIDER: S-EPMC3072096 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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A fast and low-power microelectromechanical system-based non-volatile memory device.

Lee Sang Wook SW Park Seung Joo SJ Campbell Eleanor E B EE Park Yung Woo YW

Nature communications 20110101

Several new generation memory devices have been developed to overcome the low performance of conventional silicon-based flash memory. In this study, we demonstrate a novel non-volatile memory design based on the electromechanical motion of a cantilever to provide fast charging and discharging of a floating-gate electrode. The operation is demonstrated by using an electromechanical metal cantilever to charge a floating gate that controls the charge transport through a carbon nanotube field-effect ...[more]

PMID: 21364559

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Project description:Smart surveillance systems are used to monitor specific areas, such as homes, buildings, and borders, and these systems can effectively detect any threats. In this work, we investigate the design of low-cost multiunit surveillance systems that can control numerous surveillance cameras to track multiple objects (i.e., people, cars, and guns) and promptly detect human activity in real time using low computational systems, such as compact or single board computers. Deep learning techniques are employed to detect certain objects to surveil homes/buildings and recognize suspicious and vital events to ensure that the system can alarm officers of relevant events, such as stranger intrusions, the presence of guns, suspicious movements, and identified fugitives. The proposed model is tested on two computational systems, specifically, a single board computer (Raspberry Pi) with the Raspbian OS and a compact computer (Intel NUC) with the Windows OS. In both systems, we employ components, such as a camera to stream real-time video and an ultrasonic sensor to alarm personnel of threats when movement is detected in restricted areas or near walls. The system program is coded in Python, and a convolutional neural network (CNN) is used to perform recognition. The program is optimized by using a foreground object detection algorithm to improve recognition in terms of both accuracy and speed. The saliency algorithm is used to slice certain required objects from scenes, such as humans, cars, and airplanes. In this regard, two saliency algorithms, based on local and global patch saliency detection are considered. We develop a system that combines two saliency approaches and recognizes the features extracted using these saliency techniques with a conventional neural network. The field results demonstrate a significant improvement in detection, ranging between 34% and 99.9% for different situations. The low percentage is related to the presence of unclear objects or activities that are different from those involving humans. However, even in the case of low accuracy, recognition and threat identification are performed with an accuracy of 100% in approximately 0.7 s, even when using computer systems with relatively weak hardware specifications, such as a single board computer (Raspberry Pi). These results prove that the proposed system can be practically used to design a low-cost and intelligent security and tracking system.

Dataset Information

A fast and low-power microelectromechanical system-based non-volatile memory device.

Publications

A fast and low-power microelectromechanical system-based non-volatile memory device.

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