1/ f Noise and Machine Intelligence in a Nonlinear Dopant Atom Network

Tao Chen, Peter A. Bobbert, Wilfred G. van der Wiel*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
73 Downloads (Pure)

Abstract

Noise exists in nearly all physical systems ranging from simple electronic devices such as transistors to complex systems such as neural networks. To understand a system's behavior, it is vital to know the origin of the noise and its characteristics. Recently, it was shown that the nonlinear electronic properties of a disordered dopant atom network in silicon can be exploited for efficiently executing classification tasks through “material learning.” Here, we study the dopant network's intrinsic 1/f noise arising from Coulomb interactions, and its impact on the features that determine its computational abilities, viz., the nonlinearity and the signal-to-noise ratio (SNR), is investigated. The findings on optimal SNR and nonlinear transformation of data by this nonlinear network provide a guideline for the scaling of physical learning machines and shed light on neuroscience from a new perspective.

Original languageEnglish
Article number2000014
JournalSmall Science
Volume1
Issue number3
DOIs
Publication statusPublished - 15 Jan 2021

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