Prevention of integrated circuit counterfeiting through logic locking faces the fundamental challenge of securing an obfuscation key against physical and algorithmic threats. Previous work has focused on strengthening the logic encryption to protect the key against algorithmic attacks but failed to provide adequate physical security. In this work, we propose a logic locking scheme that leverages the non-volatility of the nanomagnet logic (NML) family to achieve both physical and algorithmic security. Polymorphic NML minority gates protect the obfuscation key against algorithmic attacks, while a strain-inducing shield surrounding the nanomagnets provides physical security via a self-destruction mechanism, securing against invasive attacks. We experimentally demonstrate that shielded magnetic domains are indistinguishable, securing against imaging attacks. As NML suffers from low speeds, we propose a hybrid CMOS logic scheme with embedded obfuscated NML “islands”. The NML secures the functionality of sensitive logic while CMOS drives the timing-critical paths.
We propose a four-terminal domain wall-magnetic tunnel junction (DW-MTJ) neuron that enables the first-ever purely spintronic multilayer perceptron with unsupervised learning. The leaky integrate-and-fire neuron has a ferromagnetic DW track coupled to a binary MTJ by an electrically insulated layer. Current through the DW track performs integration by moving the DW. Leaking occurs by moving the DW in the opposite direction of integration due to either dipolar magnetic field, anisotropy gradient, or shape variation. When the DW passes underneath the MTJ, it fires by switching between the resistive and conductive states.
In a crossbar perceptron, the DW track of each neuron is connected to the analog three-terminal DW-MTJ synapses and the MTJ terminals cascade multiple layers. Finally, an unsupervised learning algorithm results from the feedback between the neuron MTJ and the analog synapses, providing best results of 98.11% accuracy on the Wisconsin breast cancer clustering task.
Writing magnetic random-access memory (MRAM) by ultrafast and energy-efficient spin-orbit torque (SOT) has been impeded by the orthogonality between spin polarization and thermally stable perpendicular magnetic anisotropy (PMA). Previously proposed approaches to break this symmetry increase the fabrication complexity, are highly sensitive to the SOT current duration and magnitude, or increase the switching energy. To overcome these challenges, we exploit the precessional nature of the field-like SOT to propose a toggle PMA SOT-MRAM with simple structure that is controlled by a unidirectional SOT current. The proposed MRAM achieves field-free and energy-efficient switching that is robust to variations in the SOT current magnitude and duration with greater than 50% tolerance demonstrated through micromagnetic simulation. The deformation-free structure provides efficient data read-out and can be leveraged for directional writing through a simple XOR between the stored and incoming bits.
Neuromorphic computing captures the quintessential neural behaviors of the brain and is a promising candidate for the beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. The neuronal lateral inhibition feature, closely associated with the biological receptive field, is crucial to neuronal competition in the nervous system as well as its neuromorphic hardware counterpart. The domain wall - magnetic tunnel junction (DW-MTJ) neuron is an emerging spintronic artificial neuron device exhibiting intrinsic lateral inhibition. This work discusses lateral inhibition mechanism of the DW-MTJ neuron and shows by micromagnetic simulation that lateral inhibition is efficiently enhanced by the Dzyaloshinskii-Moriya interaction (DMI).
Advances in machine intelligence have sparked interest in hardware accelerators to implement these algorithms, yet embedded electronics have stringent power, area budgets, and speed requirements that may limit non- volatile memory (NVM) integration. In this context, the development of fast nanomagnetic neural networks using minimal training data is attractive. Here, we extend an inference-only proposal using the intrinsic physics of domain-wall MTJ (DW-MTJ) neurons for online learning to implement fully unsupervised pattern recognition operation, using winner-take-all networks that contain either random or plastic synapses (weights). Meanwhile, a read-out layer trains in a supervised fashion. We find our proposed design can approach state-of-the-art success on the task relative to competing memristive neural network proposals, while eliminating much of the area and energy overhead that would typically be required to build the neuronal layers with CMOS devices.
The challenge of developing an efficient artificial neuron is impeded by the use of external CMOS circuits to perform leaking and lateral inhibition. The proposed leaky integrate-and-fire neuron based on the three terminal magnetic tunnel junction (3T-MTJ) performs integration by pushing its domain wall (DW) with spin-transfer or spin-orbit torque. The leaking capability is achieved by pushing the neurons’ DWs in the direction opposite of integration using a stray field from a hard ferromagnet or a non-uniform energy landscape resulting from shape or anisotropy variation. Firing is performed by the MTJ stack. Finally, analog lateral inhibition is achieved by dipolar field repulsive coupling from each neuron. An integrating neuron thus pushes slower neighboring neurons’ DWs in the direction opposite of integration. Applying this lateral inhibition to a ten-neuron output layer within a neuromorphic crossbar structure enables the identification of handwritten digits with 94% accuracy.
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