Memristors, when utilized as electronic components in circuits, can offer opportunities for the implementation of novel reconfigurable electronics. While they have been used in large arrays, studies in ensembles of devices are comparatively limited. Here we propose a vertically stacked memristor configuration with a shared middle electrode. We study the compound resistive states presented by the combined in-series devices and we alter them either by controlling each device separately, or by altering the full configuration, which depends on selective usage of the middle floating electrode. The shared middle electrode enables a rare look into the combined system, which is not normally available in vertically stacked devices. In the course of this study it was found that separate switching of individual devices carries over its effects to the complete device (albeit non-linearly), enabling increased resistive state range, which leads to a larger number of distinguishable states (above SNR variance limits) and hence enhanced device memory. Additionally, by applying a switching stimulus to the external electrodes it is possible to switch both devices simultaneously, making the entire configuration a voltage divider with individual memristive components. Through usage of this type of configuration and by taking advantage of the voltage division, it is possible to surge-protect fragile devices, while it was also found that simultaneous reset of stacked devices is possible, significantly reducing the required reset time in larger arrays.
Biological synapses store multiple memories on top of each other in a palimpsest fashion and at different timescales. Palimpsest consolidation is facilitated by the interaction of hidden biochemical processes that govern synaptic efficacy during varying lifetimes. This arrangement allows idle memories to be temporarily overwritten without being forgotten, in favour of new memories utilised in the short-term. While embedded artificial intelligence can greatly benefit from such functionality, a practical demonstration in hardware is still missing. Here, we show how the intrinsic properties of metal-oxide volatile memristors emulate the hidden processes that support biological palimpsest consolidation. Our memristive synapses exhibit an expanded doubled capacity which can protect a consolidated long-term memory while up to hundreds of uncorrelated short-term memories temporarily overwrite it. The synapses can also implement familiarity detection of previously forgotten memories. Crucially, palimpsest operation is achieved automatically and without the need for specialised instructions. We further demonstrate a practical adaptation of this technology in the context of image vision. This showcases the use of emerging memory technologies to efficiently expand the capacity of artificial intelligence hardware towards more generalised learning memories.
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