Browsing by Subject "Memristors"

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Open Access
Learning and inference for wireless communications applications using in-memory analog computing
(2024-07) Ali, Muhammad Atif
The exponential growth of wireless communication technologies has created a crucial need for more efficient and intelligent signal processing in decentralized devices and systems. Traditional digital computing architectures increasingly struggle to meet these rising computational demands, leading to performance bottlenecks and energy inefficiencies. The problem becomes more significant on edge devices with limited computing capabilities and severe energy limitations. Integrating machine learning algorithms with in-memory analog computing, specifically memristor-based architectures, provides a non-traditional computing paradigm and can potentially enhance the energy efficiency of edge devices. By leveraging the properties of memristors, which can perform both storage and computation, this research investigates ways to potentially reduce latency and power consumption in signal-processing tasks for wireless communications. This study examines memristor-based analog computing for deep learning and inference in three areas of (wireless) communications: cellular network traffic prediction, multi-sensor over-the-air inference for internet-of-things devices, and neural successive cancellation decoding for polar codes. The research includes the development of robust training techniques for memristive neural networks to cater for degraded performance due to noise in analog computations and offer acceptable prediction accuracy with reduced computational overhead for network traffic management. It explores in-memory computing for an Lp-norm inspired sensor fusion method with analog sensors and enables more efficient multi-sensor data fusion. Also, it investigates the incorporation of analog memristive computing in neural successive cancellation decoders for polar codes, which could lead to more energy-efficient decoding algorithms. The findings of the thesis suggest potential improvements in energy efficiency and provide insights into the benefits and limitations of using in-memory computing for wireless communication applications.
Open Access
Memristive behavior in a junctionless flash memory cell
(American Institute of Physics Inc., 2015) Orak, I.; Ürel, M.; Bakan, G.; Dana, A.
We report charge storage based memristive operation of a junctionless thin film flash memory cell when it is operated as a two terminal device by grounding the gate. Unlike memristors based on nanoionics, the presented device mode, which we refer to as the flashristor mode, potentially allows greater control over the memristive properties, allowing rational design. The mode is demonstrated using a depletion type n-channel ZnO transistor grown by atomic layer deposition (ALD), with HfO2 as the tunnel dielectric, AI2O3 as the control dielectric, and non-stoichiometric silicon nitride as the charge storage layer. The device exhibits the pinched hysteresis of a memristor and in the unoptimized device, R off/R on ratios of about 3 are presented with low operating voltages below 5 V. A simplified model predicts Roff/Ron ratios can be improved significantly by adjusting the native threshold voltage of the devices. The repeatability of the resistive switching is excellent and devices exhibit 106 s retention time, which can, in principle, be improved by engineering the gate stack and storage layer properties. The flashristor mode can find use in analog information processing applications, such as neuromorphic computing, where well-behaving and highly repeatable memristive properties are desirable.