# Origin of current-controlled negative differential resistance modes and the emergence of composite characteristics with high complexity

## TL;DR

This paper introduces a material-independent model explaining current-controlled negative differential resistance, highlighting its fundamental characteristics and potential for engineering complex behaviors in neuromorphic computing devices.

## Contribution

A novel, material-independent model of negative differential resistance that accounts for non-uniform current distribution and explains complex composite behaviors.

## Key findings

- Model accurately predicts discontinuous snap-back response.
- Continuous and discontinuous NDR serve as fundamental building blocks.
- Potential for designing advanced neuromorphic electronic components.

## Abstract

Current-controlled negative differential resistance has significant potential as a fundamental building block in brain-inspired neuromorphic computing. However, achieving desired negative differential resistance characteristics, which is crucial for practical implementation, remains challenging due to little consensus on the underlying mechanism and unclear design criteria. Here, we report a material-independent model of current-controlled negative differential resistance to explain a broad range of characteristics, including the origin of the discontinuous snap-back response observed in many transition metal oxides. This is achieved by explicitly accounting for a non-uniform current distribution in the oxide film and its impact on the effective circuit of the device, rather than a material-specific phase transition. The predictions of the model are then compared with experimental observations to show that the continuous S-type and discontinuous snap-back characteristics serve as fundamental building blocks for composite behaviour with higher complexity. Finally, we demonstrate the potential of our approach for predicting and engineering unconventional compound behaviour with novel functionality for emerging electronic and neuromorphic computing applications.

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Source: https://tomesphere.com/paper/1907.02651