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Binary stochastic neurons (BSNs) are excellent activators for machine learning. An ideal platform for implementing them are 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&#x0027;s activation function, the pinning current (which pins the output to a particular binary state), and the response time &#x2013; all exhibit strong sensitivity to very slight geometric variations in the LBM&#x0027;s cross-section. A mere 1&#x0025; change in the diameter of a circular nanomagnet in any arbitrary direction can alter the response time by a factor of &#x223C;4 at room temperature and a 10&#x0025; change can alter the pinning current by a factor of &#x223C;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.

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