DOI: https://doi.org/10.29363/nanoge.neuronics.2024.019
Publication date: 18th December 2023
Emerging memristor-based neuromorphic computing architectures heavily rely on the proper control of intrinsic fluctuations in the building blocks. In most neural network applications, the improper device noise prevents fine control over synaptic weights encoded in memristive conductances, and dedicated denoising techniques are needed for improved bit resolution [1]. In contrast, other schemes, such as memristive Hopfield neural networks, rely on a probabilistic computational approach, where device noise can be harvested as a resource for efficient convergence to a global solution of the targeted problem [2,3].
Here, we present a thorough analysis of the 1/f-type noise properties of various memristive systems [4-6]. We demonstrate detailed noise tailoring considerations, i.e. the dependence of the steady state noise amplitudes on the material choice, the junction size, the transport mechanism, and the characteristic geometry of the active volume. In addition to the steady-state noise properties, we present a method for full-switching-cycle nonlinear noise spectroscopy. This approach traces the transformation of current-voltage nonlinearities into nonlinear noise spectra, providing a unique tool to identify the relevant source of fluctuations in the transport model. Furthermore, noise measurements over the entire switching cycles reveal voltage-manipulated noise contributions in the non-steady-state regime, i.e. a cycle-to-cycle redistribution of the fluctuators around the device bottleneck. This feature highlights the rather strong cycle-to-cycle variation of noise in apparently reproducible resistive switches, while also allowing the selection of optimized junction states with highly denoised characteristics. With these studies, we demonstrate the merits of advanced noise spectroscopy, which not only provides a fundamental understanding of the sources of fluctuations, but also lays the foundation for noise reduction strategies for high precision neuromorphic applications.
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