Fig. 4: Reinforcement learning optimization results for spin–orbit torque (SOT) and spin transfer torque (STT) devices. | Communications Engineering

Fig. 4: Reinforcement learning optimization results for spin–orbit torque (SOT) and spin transfer torque (STT) devices.

From: AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators

Fig. 4

a, b PDF comparison of top 5 device configurations, best configuration, pseudo-random number generator (PRNG), and target distribution for both SOT and STT devices. c, d Parameter configurations of top 5 devices with energy and Kullback-Leibler (KL) divergence metrics compared against the default configurations and a PRNG for both SOT and STT devices. e, f Pareto fronts comparing energy and KL divergence metrics of various SOT and STT device configurations. g, h Probability distributions of the parameter ranges that were explored for both SOT and STT devices.

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