The Strong Sensitivity of the Characteristics of Binary Stochastic Neurons Employing Low Barrier Nanomagnets to Small Geometrical Variations

Binary stochastic neurons (BSNs) are excellent activators for machine learning. An ideal platform for implementing them is low-or zero-energy-barrier nanomagnets (LBMs) possessing in-plane anisotropy (e.g., circular or slightly elliptical disks) whose fluctuating magnetization encodes a probabilistic (p-) bit. Here, we show that such a BSN's activation function, the pinning current (which pins the output to a particular binary state), and the response time - all exhibit strong sensitivity to very slight geometric variations in the LBM's cross-section. A mere 1% change in the diameter of a circular nanomagnet in any arbitrary direction can alter the response time by a factor of -4 at room temperature and a 10% change can alter the pinning current by a factor of -2. All this causes large device-to-device variation which is detrimental to integration. we also show that the energy dissipation is lowered but the response time is increased by replacing a circular cross-section with a slightly elliptical one and then encoding the p-bit in the magnetization component along the major axis. Encoding the p-bit in the magnetization component along the minor axis has the opposite effect. The energy-delay-product, however, is relatively independent of whether the cross-section is a circle or an ellipse and which magnetization component encodes the p-bit in the case of the ellipse.

Automated summary

Binary stochastic neurons (BSNs) are excellent activators for machine learning. Low or zero-energy-barrier nanomagnets (LBMs) with slight geometric variations can affect the activation function, pinning current and response time of BSNs, leading to large device-to-device variation and impacting integration.