Coupled Spin-Torque Nano-Oscillator-Based Computation

In this article, we present a comprehensive study of four frequency locking mechanisms in Spin Torque Nano Oscillators (STNOs) and explore their suitability for a class of specialized computing applications. We implemented a physical STNO model based on Landau-Lifshitz-Gilbert-Slonczewski equation and benchmarked the model to experimental data. Based on our simulations, we provide an in-depth analysis of how the “self-organizing” ability of coupled STNO array can be effectively used for computations that are unsuitable or inefficient in the von-Neumann computing domain. As a case study, we demonstrate the computing ability of coupled STNOs with two applications: edge detection of an image and associative computing for image recognition. We provide an analysis of the scaling trends of STNOs and the effectiveness of different frequency locking mechanisms with scaling in the presence of thermal noise. We also provide an in-depth analysis of the effect of variations on the four locking mechanisms to find the most robust one in the presence of variations.

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