Developing a room temperate, self-calibrating standard for electrical resistance
The revised SI unleashes the prospect of the ‘NMI-on-a-chip’: self-calibrating devices able to independently connect measurements to fundamental constants. In the future, memristive devices might serve as such a standard for resistance, and might enable new types of computing architectures, with orders-of-magnitude lower power consumption than current complementary metal-oxide-semiconductor (CMOS) technologies.
Memristors, a portmanteau of memory and resistor, are electrical resistance switches that exhibit quantised conductance. Practical applications could exploit potential capabilities for observing such effects without the limitations of quantum-Hall effect-based resistance standards, that require vacuum, cryogenic temperatures and intense magnetic fields.
Device development has so far been hampered by a lack of reliable methods for measuring relevant physical quantities at nanoscale dimensions. This is important as memristor functionality is influenced by material purity: even parts-per-million contamination drastically affects capacitance, electronic structure, and thermodynamic properties, for instance.
The project will develop instrumentation to measure size — at the nanometre scale — and develop measurement methods to help realise quantum-based standards of resistance of memristor devices, in air, at room temperature. New theoretical models of effects within memristors will be developed to set out relationships between material properties and functionality. Moreover, characterisation methods using scanning-probe-micro-scopes will be investigated, while methods to quantify device properties, cross-platform measurement techniques and a quantum-based standard of resistance for nano-applications will be developed.
Accurate dimensional measurements and methods will support the development of self-calibrating systems. Spin-offs may also open up new developmental paths for the semi-conductor and electronics manufacturing industries, potentially unlocking powerful new computing capabilities offering breakthrough performance for applications like artificial intelligence and machine learning.