Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session P34: Recent Advances on Spintronics-based Computing: from Deterministic to ProbabilisticInvited
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Sponsoring Units: FIAP Chair: Ernesto Marinero, Purdue University Room: BCEC 205A |
Wednesday, March 6, 2019 2:30PM - 3:06PM |
P34.00001: Recent progress in reducing the current and time for magnetization switching in magnetic tunnel devices for memory applications Invited Speaker: Jonathan Sun In spin-transfer-torque switched magnetic random access memory (STT-MRAM) using magnetic tunnel junctions (MTJ), the STT switching current directly affects selection transistor size and circuit density. This is a critical attribute for cost competitiveness in commercial applications. Recent developments in STT-MRAM have been focusing on reducing switching current well below 100 uA, increasing speed beyond 50 ns, and improving switching reliability to better than 10-9, while simultaneously aiming for data retention of 10 years at operating temperatures ranging between -40 and + 85C or beyond. In this talk, I will review the fundamental device and materials physics that govern the STT-switching dynamics. While the switching threshold current is more directly related to data-retention, the actual operating switching current for ensuring a certain speed and reliability (switching error) for fast, below 10ns switching can also exceed macrospin expectations. Experiments show the switching current required to achieve deep error floor can be less than estimated from macrospin. For further improvements it is important to understand the dynamics involving a nanomagnet's internal degrees of magnetic freedoms during STT switching, a potentially complex subject rich in nonlinear dynamics. These complex nonlinear mechanisms can speed-up the intended switching of the MTJ, but can also affect the reference-layer stability, requiring special care. Longer term, for even faster manipulation of nanomagnets approaching or below 1 ns, new sources of spin-current with better charge-to-spin current conversion needs to be considered, such as thermal magnonic or spin-orbit-derived spin-currents. I will briefly discuss recent advances with these new sources of spin currents, and the likely common challenges they will give rise to, in terms of materials and device design and development. |
Wednesday, March 6, 2019 3:06PM - 3:42PM |
P34.00002: Spintronic Devices for Neural Networks Invited Speaker: Shunsuke Fukami Representing the human brain in computers, so-called neuromorphic computing, is one of the focuses of interest in recent electronics as the brain is a model system that can readily accomplish complex tasks at small power consumption level, in contrast to conventional von Neumann computers. An artificial neural network offers a promising approach for low-power and intelligent neuromorphic computing. A key ingredient for the network is an artificial synapse that has analog and nonvolatile memory functionalities and frequent learning capability as in the real synapse in the brain. Here we show an artificial neural network with spintronic artificial synapses, which meet these requirements [1]. We will first describe an analog spin-orbit torque (SOT) switching device consisting of an antiferromagnet/ferromagnet bilayer structure [2], which can serve as the artificial synapse. The mechanism of analog and nonvolatile property [3] will be described. Secondly, a proof-of-concept demonstration of an artificial neural network with 36 SOT devices will be shown. An associative memory operation is performed based on the Hopfield model and learning ability of the spintronic artificial synapse is confirmed [4]. |
Wednesday, March 6, 2019 3:42PM - 4:18PM |
P34.00003: Supervised learning of an artificial opto-magnetic neural network with picosecond laser
pulses Invited Speaker: Theo Rasing The explosive growth of data and its related energy consumption is pushing the need to |
Wednesday, March 6, 2019 4:18PM - 4:54PM |
P34.00004: p-Bits for Probabilistic Spin Logic Invited Speaker: Supriyo Datta The growing field of quantum computing is based on the concept of a q-bit which is a delicate superposition of 0 and 1, along with coherent coupling techniques for entangling them. By contrast a probabilistic bit or a p-bit is a robust classical entity that fluctuates between 0 and 1, and can be correlated using techniques borrowed from the field of neural networks. |
Wednesday, March 6, 2019 4:54PM - 5:30PM |
P34.00005: Bioinspired Computing Leveraging the Physics of Magnetic Nano-Oscillators Invited Speaker: Damien Querlioz Brains display many features typical of non-linear dynamical networks, such as synchronization or chaotic behavior. These observations have inspired a whole class of models that harness the power of complex non-linear dynamical networks for computing. In this framework, neurons are modeled as non-linear oscillators, and synapses as the coupling between oscillators. However, there are few hardware implementations of these systems, because large numbers of interacting non-linear oscillators are necessary. In this talk, we will see why coupled magnetic nano-oscillators are very promising for realizing cognitive computing at the nanometer scale. Then, we will present our experimental and theoretical results. We will show how speech recognition can be performed using the transient dynamics and the synchronization of a few harmonic spin torque oscillators [1]. These results highlight key opportunities and requirements for harnessing spintronic physics for bioinspired computing. We will also show how superparamagnetic oscillators can code and transform information in a robust population-type scheme [2]. These results highlight that some apparently undesirable phenomena like superparamagnetism can become compelling for bioinspired schemes. We will finally discuss how this line of research can take inspiration from both neuroscience and machine learning, and finish by open questions raised by our research. |
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