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The balanced ternary representation allows for significant performance improvement for ternary systems. Immaturity of the semiconductor industry in providing an applicable device for ternary infrastructures during the past decades is an enormous obstacle to actualize the ternary circuit realization. Among the promising devices for this purpose, carbon nanotube field effect transistor (CNFET) is qualified with outstanding characteristics which make it suitable for implementing the ternary logic functions. In this paper, for the first time, we present and evaluate the basic ternary arithmetic circuits in balanced representation based on the CNFET technology. Experimental evaluation of reviewed designs in both balanced and unbalanced representation indicates the superiority of the balanced representation in the design of ternary arithmetic blocks. [ABSTRACT FROM AUTHOR]

Recently, Multiple Valued Logic (MVL) has attracted attention, because it reduces the area and complexity of circuits when compared to binary logic. Carbon nanotube field effect transistors (CNFET) are a good candidate for implementing MVL logic circuits, since determining the threshold voltage value of CNFET is easily achievable by changing the diameter of the nanotubes which are placed under the transistor gate. In this paper, quaternary CNFET based 4–2 and 5–2 compressor cells have been proposed. The proposed cell designs were investigated using HSPICE simulator with the standard 32 nm CNFET technology under different operational conditions such as different temperatures, different load capacitances, and different supply voltages. Also the sensitivity to process variations and noise immunity of the proposed designs have been investigated. The proposed quaternary logic designs reduce the delay of the multi operand addition of arithmetic circuits by eliminating the carry propagation overhead. [ABSTRACT FROM AUTHOR]

• 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]