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• A digital fuzzy approach for image edge detection is proposed. • This method employs simple fuzzy rules for digital image edge detection. • Simple logic gates based on carbon nanotube transistor are used for implementation. • Delay and power are significantly reduced while a reasonable accuracy is provided. This study presents a novel digital fuzzy approach for image edge detection based on carbon nanotube field effect transistor (CNTFET). The proposed design employs a simple fuzzy rule for digital image edge detection. To investigate the functionality and performance of the proposed fuzzy approach, our results are compared with the MATLAB edge detection operators such as Sobel, Canny, and Prewitt. Based on the simulation results, we demonstrate that the proposed fuzzy approach can be implemented through nanoscale logic gates and provide a reasonable accuracy. The core element of the proposed fuzzy system, implemented with CNTFETs, occupies 0.22 µm2 layout area and consumes 535 nW power consumption. Our results also emphasize the benefits of the Gate-All-Around (GAA) CNTFETs as a potential candidate for nanoscale digital fuzzy image processing applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel fully programmable membership function generator (MFG) based on carbon nanotube field effect transistors (CNTFETs). The proposed CNTFET-based MFG has full control over the output characteristics such as height, and position independently. The proposed design can produce four different membership functions only by calibrating the external control bias voltages. Moreover, two peripheral transmission gates are utilized to provide controllability over the slope of the generated membership functions in a wide range. The primary advantage of the proposed MFG compared to its counterparts is the ability to control the slope of the resulted membership functions entirely and separately using only two external bias voltages without the need for changing the device physical dimensions. The simulation results demonstrate that the proposed design improves the power, delay, and layout area, on average by, 72%, 55%, and 33% as compared to the previous designs. Furthermore, the proposed MFG shows only 2.13% RMS error, and 5.65% maximum absolute error for the generated Gaussian trapezoidal membership function as compared to the ideal form. Our results certify that the proposed CNTFET-based MFG has the potential to be used as a promising platform for fuzzy inference systems in deep nanoscale technology nodes. [ABSTRACT FROM AUTHOR]