Design of efficient approximate 1-bit Full Adder cells using CNFET technology applicable in motion detector systems
| Autor: | Mehdi Bagherizadeh, Zahra Zareei, Mohammad Hossein Shafiabadi, Yavar Safaei Mehrabani |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Motion detector Adder Computer science 020208 electrical & electronic engineering Transistor Monte Carlo method General Engineering Image processing 02 engineering and technology Carbon nanotube 01 natural sciences law.invention Computer Science::Hardware Architecture Robustness (computer science) law 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Electronic engineering MATLAB computer computer.programming_language |
| Zdroj: | Microelectronics Journal. 108:104962 |
| ISSN: | 0026-2692 |
| DOI: | 10.1016/j.mejo.2020.104962 |
| Popis: | In this paper, we present two novel approximate Full Adder cells with capacitive threshold logic (CTL) using carbon nanotube field-effect transistor (CNFET) technology. To investigate the efficiency of the proposed cells, extensive simulations are carried out at both application and transistor levels. At the application level, by using the MATLAB tool, the proposed cells are applied to the motion detector algorithm as one of the practical image processing applications. Peak signal-to-noise ratio (PSNR) is considered as a legitimate application-level evaluating factor. In addition, by using the HSPICE tool, the hardware level parameters such as average power consumption, delay, and power-delay product (PDP) are estimated. Then, a compromise between the application and hardware-level metrics is considered. Noise analysis is also accomplished by taking noise immunity curve (NIC) and average noise threshold energy (ANTE) into account. Furthermore, Monte Carlo transient analysis is exploited to study the robustness of the introduced cells against diameter deviations of the carbon nanotubes (CNTs). Simulation results indicate the supremacy of the proposed cells compared to others. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect