Superparamagnetic tunnel junctions - true randomness, electrical coupling and neuromorphic computing

This research deals with superparamagnetic tunnel junctions (SMTJs), encompassing their fabrication, characterization and potential applications in the context of neuromorphic computing and as a random number generator. Magnetic tunnel junctions (MTJs) based on a magnesium oxide barrier between cobalt iron-boron alloys exhibit a significant tunnel magnetoresistance (TMR) effect, typically on the order of 100−200 %. This characteristic high TMR signal has led to their widespread commercial use in sensing or as magnetoresistive random access memory (MRAM). Here, our developed TMR stack with in-plane magnetization exhibits a TMR ratio of over 200 % at a resistance area product of 550 Ωµm2. Within this work at JGU Mainz, the first successful MTJ nanopillar fabrication was accomplished in the group, and numerous optimization steps have been undertaken for the development of superparamagnetic tunnel junctions exhibiting nanosecond fluctuations. In the superparamagnetic regime, the magnetization of the ferromagnetic “free layer” fluctuates solely due to thermal excitation, resulting in a volatile MTJ. This inherent fluctuation, occurring naturally, can serve as an entropy source for random number generators, which makes stochastic MTJs attractive for applications with demanding requirements on random number generation, such as Monte Carlo simulations. In this work, it is demonstrated that a random number generator based on SMTJs exhibits true randomness of nanosecond time scale when combined with logic XOR gates. The quality of true random bit generation is assessed by evaluating all randomness tests of the statistical test suite provided by the National Institute of Standards and Technology (NIST). The fluctuation rate and state probability can be manipulated by external magnetic fields, applied currents or voltages, or by the temperature. Electrons that cross the tunnel barrier transfer a torque to the magnetization of the ferromagnetic free layer due to their spin (referred to as “spin-transfer-torque”) and thus significantly influencing the stochastic behavior of the SMTJ. In addition, the Joule heating affects the fluctuation rate at high current densities. It is demonstrated that both contributions, the Joule heating and the effect of the “spin-transfer-torque”, can be determined from dwell time analysis. Furthermore, coupling in the switching behavior can arise when two or more stochastic MTJs are electrically connected. In this work, the coupling strength between two SMTJs has been analyzed using the cross-correlation of the voltage fluctuation as a function of the applied source voltage. This approach was both simulated and experimentally verified using time series measurements for two stochastic MTJs. A network of multiple SMTJs can also be used to generate a Gaussian probability distribution, which might potentially be beneficial for noise-based neuromorphic computing approaches. At the end of this thesis, a neuromorphic circuit implementation based on SMTJs, diodes and transistors is presented. This implementation allows for an analog computation of a noise based local learning algorithm (node perturbation) in a neuromorphic hardware. The proposed approach represents a hardware-based alternative to the established backpropagation algorithm, since the neural network enables an analog and local calculation of the weight adjustment by a learning rule called “node perturbation”.xvi, 166 Seiten ; Illustrationen, Diagramm

Electrical coupling of superparamagnetic tunnel junctions mediated by spin-transfer-torques

In this work, the effect of electrical coupling on stochastic switching of two in-plane superparamagnetic tunnel junctions (SMTJs) is studied, using experimental measurements as well as simulations. The coupling mechanism relies on the spin-transfer-torque (STT) effect, which enables the manipulation of the state probability of an SMTJ. Through the investigation of time-lagged cross-correlation, the strength and direction of the coupling are determined. In particular, the characteristic state probability transfer curve of each SMTJ leads to the emergence of a similarity or dissimilarity effect. The cross-correlation as a function of applied source voltage reveals that the strongest coupling occurs for high positive voltages for our SMTJs. In addition, we show state tuneability as well as coupling control by the applied voltage. The experimental findings of the cross-correlation are in agreement with our simulation results

Strain-induced Shape Anisotropy in Antiferromagnetic Structures

We demonstrate how shape-induced strain can be used to control antiferromagnetic order in NiO/Pt thin films. For rectangular elements patterned along the easy and hard magnetocrystalline anisotropy axes of our film, we observe different domain structures and we identify magnetoelastic interactions that are distinct for different domain configurations. We reproduce the experimental observations by modeling the magnetoelastic interactions, considering spontaneous strain induced by the domain configuration, as well as elastic strain due to the substrate and the shape of the patterns. This allows us to demonstrate and explain how the variation of the aspect ratio of rectangular elements can be used to control the antiferromagnetic ground state domain configuration. Shape-dependent strain does not only need to be considered in the design of antiferromagnetic devices, but can potentially be used to tailor their properties, providing an additional handle to control antiferromagnets