The pressing need for beyond-von-Neumann computing paradigms has triggered intensive efforts into understanding and controlling various resistive switching mechanisms. This switching, in which the two-terminal resistance of a device is controlled by current is at the heart of emerging technologies such as resistive random access memory and neuromorphic computation. These technologies promise to revolutionize artificial neural networks and mimic the behavior of biological brains, triggering a race the for optimal resistive switching materials.
In Mott insulators electrical currents can change resistance by orders-of-magnitude due to a phase transition from insulating to metallic states. The volatility of switching in Mott insulators can be adjusted by several tuning parameters, enabling both memory devices and neuron-like functionalities. Moreover, Mott insulators have potential for extremely fast switching timescales and energy efficiency. These unique properties have made Mott insulators prominent candidate materials for resistive switching applications. However, the physical mechanisms behind resistive switching in these materials are poorly understood and not easily controllable, hampering advancement in this field.
We suggest two main routes towards Mott-based resistive switching with ultrahigh energy efficiency. The first is by switching purely in the electronic sector while minimizing structural distortions. The low heat capacity of electrons may enable switching with a fraction of the energy required in an insulator-metal transition coupled to structural transition. The second is absorption of latent heat and/or elastic energy from the surroundings of the switching element, thus reducing the externally supplied power consumption. Our aim is to shed light on the basic mechanisms governing the phase transition in Mott insulators and use defects, doping and strain engineering to understand and tune the switching mechanisms. This may allow for novel functionalities and ultralow energy consumption.
