Characterization and Modeling of Spin-Transfer Torque (STT) Magnetic Memory for Computing Applications

Abstract

AbstractWith the ubiquitous diffusion of mobile computing and Internet of Things (IoT), the amount of data exchanged and processed over the internet is increasing every day, demanding secure data communication/storage and new computing primitives. Although computing systems based on microelectronics steadily improved over the past 50 years thanks to the aggressive technological scaling, their improvement is now hindered by excessive power consumption and inherent performance limitation associated to the conventional computer architecture (von Neumann bottleneck). In this scenario, emerging memory technologies are gaining interest thanks to their non-volatility and low power/fast operation. In this chapter, experimental characterization and modeling of spin-transfer torque magnetic memory (STT-MRAM) are presented, with particular focus on cycling endurance and switching variability, which both present a challenge towards STT-based memory applications. Then, the switching variability in STT-MRAM is exploited for hardware security and computing primitives, such as true-random number generator (TRNG) and stochastic spiking neuron for neuromorphic and stochastic computing.