Abstract
The incessant down scaling of CMOS technology has been the main driving force for the semiconductor industry over the past decades. Yet, as process variations and leakage current continue to exhibit more pronounced effect with every technology node, this down scaling paradigm is expected to saturate in the few coming years. This prospect has led the research community to seek new technologies to surpass those challenges. Amongst the promising candidates is the memristor technology recently characterized by HP Labs. The miniaturized features and the peculiar behavior exhibited by the memsitor make it very well suited in some applications. For instance, memrsitors are used as memory cells in state-of-the-art memories known as Resistive RAMs in which the non-volatility of the memristor is exploited. The programmable nature of the memristor has made it a powerful candidate in neuromorphic and fuzzy systems that, in essence, go beyond the classical Von Neumann computing paradigm. In such systems, ideas from Artificial Intelligence, that for so long have been implemented on the software level, are implemented as electronic circuitry which renders benefits such as compact area and reduced power consumption. This work focuses on memrsitor-based Fuzzy applications. First, memristor-based Min-Max circuit used in the Fuzzy Inference engine is analyzed. It is proven that memrsitor-based Min-Max circuits can be extended to an arbitrary number of inputs ‘N’ under the proper design constraints. In addition, the effect of the memristor threshold is analyzed and a closed form expression is derived. It is shown that, for a given memristor with a specific OFF resistance and threshold current, there is a trade-off between the size and the resolution of the circuit. Then, a memrsitor-based Defuzzifier circuit is proposed. A major challenge in Defuzzifiers is their area occupancy due to the use of Multiplier and Divider circuits. In this design, the memrsitor analog programmability is leveraged to reduce the multiplication operation into simple Ohm’s Law which alleviates the need for dedicated hardware for multiplier circuit and, accordingly, reduces the area occupancy.
Department
Electronics & Communications Engineering Department
Degree Name
MS in Electronics & Communication Engineering
Graduation Date
6-1-2016
Submission Date
May 2016
First Advisor
Abdelazeem, Sherif
Committee Member 1
Soliman, Ahmed
Committee Member 2
Anis, Mohab
Extent
63 p.
Document Type
Master's Thesis
Rights
The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy.
Institutional Review Board (IRB) Approval
Not necessary for this item
Recommended Citation
APA Citation
Amer, S.
(2016).Modeling and design of memristor-based fuzzy systems [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/263
MLA Citation
Amer, Sherif Hassanein Hamed. Modeling and design of memristor-based fuzzy systems. 2016. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/263