Magnetic Tunnel Junctions For Next Generation Of Conventional And Unconventional Computing Schemes
2022-09
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Magnetic Tunnel Junctions For Next Generation Of Conventional And Unconventional Computing Schemes
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2022-09
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Magnetic tunnel junctions (MTJs) have several novel features that make them promising devices for next generation memory and computing technologies. These features include multifunctionality, tunable stochasticity, and capability of being tuned by multiple forces. In this dissertation, I demonstrate how these properties make MTJs promising solutions in conventional memory and logic applications as well as unconventional computing applications such as probabilistic bits with increased information capacity and stochastic computing units. Devices that generate random asynchronous, or telegraphic, switching signals have been proposed as probabilistic bits (p-bits) in new paradigms of probabilistic computing schemes for advanced computation. For the first part of my dissertation, I demonstrate that tunable telegraphic switching signals can be generated from MTJs through the combination of an external magnetic field and a DC bias voltage. Previous studies show that tunable telegraphic signals can be generated on MTJs with low thermal stability using only a single bias current or bias voltage. However, my results show that this ‘dual-biasing’ method has a unique capability called two-degrees of tunability, which gives this method two key advantages over the single biased method. One is that it can overcome the challenges imposed by the effects of device variations in large-scale networks and the second is that the signals generated have two times more information capacity than those generated by single-biased MTJs.
In the next part of my dissertation, I explore the interplay between the effects of the voltage-controlled exchange coupling (VCEC) and spin-orbit torque (SOT) switching mechanisms. Previous experimental work from our group has demonstrated that VCEC switching can be achieved in perpendicularly magnetized MTJs (p-MTJs) at switching current densities nearly one order of magnitude lower than those for spin transfer torque (STT) and SOT switching. In this dissertation, I show that by combining the SOT and VCEC effects, the VCEC switching current density can be reduced even further, thus providing a pathway to optimize the performance of future magnetoresistive random access memory (MRAM) technologies based on VCEC switching.
In the next chapter, I describe a method that was invented by our research group that performs stochastic computing within the hardware for computational random-access memory (CRAM), which is called SC-CRAM. Stochastic computing has several attractive capabilities such as performing complex arithmetic functions with a small number of logic gates, noise resilience, and error tolerance. However, there are significant costs in circuit area and energy consumption for the hardware required to generate stochastic bit-streams. The method described in my dissertation overcomes these costs by embedding the bit-stream generation and computation steps within the same CRAM cells. Furthermore, SC-CRAM shows significant reductions in circuit area for certain neuromorphic computing tasks when compared to conventional computing methods in CRAM.
Finally, I study the prospects of MTJs for future applications involving high radiation environments, such as space exploration. Ionizing radiation levels beyond 10 krad have detrimental effects on modern CMOS technology. However, my results demonstrate that MTJs can be exposed to ionizing radiation levels as high as 1 Mrad without significantly influencing the properties key to their performance in MRAM cells. This resilience to ionizing radiation makes MTJs strong candidates for future ‘rad-hard’ devices.
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University of Minnesota Ph.D. dissertation. September 2022. Major: Electrical Engineering. Advisor: Jian-Ping Wang. 1 computer file (PDF); x, 232 pages.
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Zink, Brandon. (2022). Magnetic Tunnel Junctions For Next Generation Of Conventional And Unconventional Computing Schemes. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/258911.
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