Date of Award
6-2024
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2024
Department
Electrical and Computer Engineering
First Advisor
Dat Tran
Abstract
The need for efficient memory solutions is ever-increasing in the contemporary computing technology landscape, particularly in neuromorphic computing. This thesis introduces a novel approach to memory storage using memcapacitors, which store information via capacitance rather than voltage, offering a promising alternative to traditional memory systems. Through the development and simulation of memcapacitor technologies, this work explores their potential to enhance machine learning capabilities and reduce power consumption. Our research demonstrates that memcapacitors can be effectively implemented at the nano-scale, albeit with substantial manufacturing investment. We employed various optimization strategies, including circuit adjustments and behavioral modeling simulations, to maximize the efficiency and scalability of these devices for traditional and neuromorphic applications. The results indicate that memcapacitors hold significant promise for advancing power-efficient computing, positioning them as a critical technology for future research and application in electrical and computer engineering. Further advancements in this technology could lead to significant breakthroughs in AI applications, especially in areas requiring dense, efficient memory architectures. Moreover, the scalability of memcapacitors paves the way for their integration into a broader range of electronic devices, potentially transforming the landscape of consumer electronics by enhancing the capabilities and efficiency of smart devices.
Recommended Citation
Anderson, Sam, "Storage Solutions within Memory Capacitor Technologies" (2024). Electrical and Computer Engineering Senior Theses. 101.
https://scholarcommons.scu.edu/elec_senior/101
