Abstract
Resistive random-access memories, also known as memristors, whose resistance can be modulated by the electrically driven formation and disruption of conductive filaments within an insulator, are promising candidates for neuromorphic applications due to their scalability, low-power operation and diverse functional behaviors. However, understanding the dynamics of individual filaments, and the surrounding material, is challenging, owing to the typically very large cross-sectional areas of test devices relative to the nanometer scale of individual filaments. In the present work, conductive atomic force microscopy is used to study the evolution of conductivity at the nanoscale in a fully CMOS-compatible silicon suboxide thin film. Distinct filamentary plasticity and background conductivity enhancement are reported, suggesting that device behavior might be best described by composite core (filament) and shell (background conductivity) dynamics. Furthermore, constant current measurements demonstrate an interplay between filament formation and rupture, resulting in current-controlled voltage spiking in nanoscale regions, with an estimated optimal energy consumption of 25 attojoules per spike. This is very promising for extremely low-power neuromorphic computation and suggests that the dynamic behavior observed in larger devices should persist and improve as dimensions are scaled down.
Highlights
The power consumption of conventional, transistor-based computers is unsustainably high, with the burgeoning of fields such as neuromorphic computing and machine learning (Thompson et al, 2021)
The behavior of many RRAM devices is dependent on the formation of localized filaments in the active layer, rather than a bulk effect (Yang et al, 2012; Lanza, 2014; Baeumer et al, 2015; Bousoulas et al, 2015; Lanza et al, 2019)
We have demonstrated the use of conductive atomic force microscopy (CAFM) as a tool for studying the plasticity and neuromorphic dynamics of SiOx at the nanoscale
Summary
The power consumption of conventional, transistor-based computers is unsustainably high, with the burgeoning of fields such as neuromorphic computing and machine learning (Thompson et al, 2021). The power density of typical supercomputer might be in the order of 100 Wcm−2. This is in comparison to a human brain, which can complete hugely complex cognitive tasks with a fraction of the cost, at around 10 mW cm−2 (Young et al, 2019). Binary oxides are promising materials for low power, high packing density resistance switching devices (sometimes referred to as memristors, or resistive random access memory, RRAM), with the potential for both non-volatile memory applications, and in-memory and neuromorphic computation (Kim et al, 2011; Ielmini, 2016; Ielmini, 2018). RRAM devices and arrays have been used to perform vector matrix multiplication, to implement training
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
