Mutually coupled superparamagnetic tunnel junctions

Abstract

Magnetic tunnel junctions consist of two ferromagnetic layers separated by a thin insulating layer. The relative orientation of the two layers' magnetization has two stable configurations: parallel or antiparallel (P or AP). For use as a binary non-volatile memory in magnetic random access memory (MRAM), the retention time in each of the two configurations needs to be on the order of several years. This corresponds to energy barriers larger than 40 kT (k is the Boltzmann constant, and T=300 K is room temperature). If the energy barrier becomes smaller than 12 kT, thermal fluctuations induce random switching of the magnetization between the P and the AP states with a mean timescale between 10 µs and 1 ms. Such fluctuating, low-energy-barrier tunnel junctions are referred to as superparamagnetic tunnel junctions [1].Recently, superparamagnetic tunnel junctions have become attractive as compact and energy-efficient devices for applications that require truly random variables [2,3], including innovative computing schemes [4,5,6,7,8]. Through adapted circuit co-design approaches, we previously showed that those devices are promising for implementing artificial neural networks with reduced energy consumption [9]. In cognitive computing, these superparamagnetic tunnel junctions can be used to emulate biological neurons [10, 11]. Here, we investigate simple ways of coupling these devices to extend these innovative computing schemes.To mimic neuron-like interconnections, it is important to develop simple schemes to couple such devices. Significant work has been done to develop coupling strategies for magnetic systems such as harmonic spin-torque nano-oscillators, which are useful for cognitive computing [12,13,14,15], but there is no equivalent scheme for superparamagnetic tunnel junctions. In this work, we experimentally demonstrate a simple electrical coupling method between two superparamagnetic tunnel junctions. We couple two stochastic magnetic junctions by placing them in parallel in a simple electrical circuit. We use the fact that their switching rate can be controlled deterministically by the applied current. They couple because their stochastic electrical transitions change the current flowing through the other device, changing its switching rate. In our circuit, the total applied current in each nanojunction is shared and depends on the joint resistance states of the nanojunctions. Depending on the current state of the system, transitions to certain joint states are favored while others are less probable.This coupling mechanism leads to non-zero cross-correlation between the stochastic switching events of the two fluctuating tunnel junctions, demonstrating their mutual coupling. We show experimentally that the cross-correlation becomes large when the switching time scales of the two superparamagnetic tunnel junctions are such that they both have roughly a 50 % probability to be in the parallel or antiparallel state. We also demonstrate that the cross-correlation increases when the total current applied to the system increases. We reach 18 % for the largest Pearson correlation value measured in the system. We find good agreements between numerical simulations of a comparable four-state Markov state process and our experiments. As we do not use sophisticated circuits for the coupling purpose, our approach opens new paths to compact and energy-efficient implementations of scaled-up arrays of coupled superparamagnetic tunnel junctions for new cognitive computing schemes. **