Study of switching dynamics and failure mechanisms in state-of-the-art SOT-MRAM

PhD          ·          Leuven        ·       Full time

Shape the future of magnetic memory: discover what happens in the first nanoseconds

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Background

Magnetic material-based memories are an emerging class of devices promising non-volatility, high-speed and low power. The first generation of memory devices, known as Spin Transfer Torque (STT) magnetic random-access memory (MRAM), are about to hit large scale production for embedded memory replacement. With a view towards second generation and later MRAM, our team is working on alternative switching mechanisms of which Spin-Orbit-Torque (SOT) and Voltage Control of Magnetic Anisotropy (VCMA) are the most promising.

State of the art and challenges

Switching speeds in the order of nanoseconds are obtained in these devices. The typical used simplified models, like the macrospin model, do not apply to these systems. There is a need for in depth characterization of the switching dynamics with time resolved measurements. Studies of when, but also why, switching failures occur are imperative for the development of these memories. Besides the typical write error rates (WER) the MRAM devices have other failure mechanism observed at high stress currents. It is expected that the self-heating significantly impacts the switching dynamics and failure mechanisms. Accurate thermal simulations and at temperature measurements are key to make a reliable characterization.

Experimental details & Methodology

To develop MRAM devices, imec uses a dedicated 300-mm wafer platform in its state-of-the-art cleanroom. The PhD candidate will take part in the characterization of these new types of devices. He/She will develop a measurement method to accurately study switching dynamics in sub-nanosecond range. This will be done in collaboration with the MRAM devices group and supervised by Simon Van Beek. In parallel, micromagnetic modelling (OOMMF, mumax) can be performed to study the switching dynamics. For data analysis and modelling, basic knowledge on programming in Python can be helpful. For modelling of the failure mechanisms, the PhD candidate can also rely on the support of the reliability expertise center within imec.

Required background: Electrical engineering, Physics

Type of work: literature study (10%), experimental work (60%), modelling (30%)

Supervisor: Bart Soree

Daily advisor: Simon Van Beek

The reference code for this position is 2023-012. Mention this reference code on your application form.

Please click here to apply.