Hugh G. Manning

TL;DR: This paper presents for the first time a distribution of junction resistance values and proves that the junction contribution to the overall resistance can be reduced beyond that of the wires through standard processing techniques, and demonstrates the important role played by the network skeleton and the specific connectivity of the network in determining network performance.

TL;DR: The authors observe the formation of a preferred conduction pathway which uses the lowest possible energy to get through the network and could be exploited for the design of optimal brain-inspired devices.

TL;DR: In this paper, the potential of resistive memory devices in resistive memories has been investigated using crystallographically controlled synthesis of SnSe nanowires, which has been published in final form at 10.1002/admi.202000474.

TL;DR: The effective medium approach provides a simple way to distinguish the sheet resistance contribution of the junctions from that of the nanowires themselves and the contrast between these two contributions determines the potential to optimize the network performance for a particular application.

TL;DR: The model predicts characteristic junction resistances that depend on the detailed connectivity of the network that can be used to compare the performance of different networks and to predict the optimum performance of any network and its scope for improvement.