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Much attention has been focused on the design of low barrier nanomagnets(LBM), whose magnetizations vary randomly in time owing to thermal noise, foruse in binary stochastic neurons (BSN) which are hardware accelerators formachine learning. The performance of BSNs depend on two important parameters:the correlation time associated with the random magnetization dynamics in aLBM, and the spin-polarized pinning current which stabilizes the magnetizationof a LBM in a chosen direction within a chosen time. Here, we show that commonfabrication defects in LBMs make these two parameters unpredictable since theyare strongly sensitive to the defects. That makes the design of BSNs with realLBMs very challenging. Unless the LBMs are fabricated with extremely tightcontrol, the BSNs which use them could be unreliable or suffer from poor yield.

Low Barrier Nanomagnet Design for Binary Stochastic Neurons: Design Challenges for Real Nanomagnets with Fabrication Defects