Classical KPFM utilises approaches such as heterodyne detection and closed-loop-bias feedback to determine electrochemical properties of the sample under investigation. Practically, this limits the KPFM measurement in terms of channels of information available (i.e., a single surface potential channel is available) and the time resolution of the measurement (e.g. ~1-10 MHz photodetector stream is down sampled to a single readout of CPD per pixel). Despite the popularity of KPFM, the level of information available (i.e. CPD) is not sufficient for systems such as electroactive materials, devices, or solid-liquid interfaces, involving nonlinear lossy dielectrics.

A new era in KPFM

In this study, the foundations are laid for a new era in KPFM utilising big data analytics. As a first step it is demonstrated that by sampling the data at sufficiently high sampling rates and using digital software based filtering and demodulation it is possible to emulate the classical KPFM aproach. However, G-Mode KPFM has several advantages over its predecessor as it negates many of the drawbacks associated with heterodyne detection and closed- loop-bias feedback, as well as significantly simplifying the technique by avoiding cumbersome instrumentation optimisation steps (i.e. lock-in parameters, feedback gains, etc.) and is immediately implementable on all atomic force microscopy platforms.

Advantages of a fully digital approach

Additional advantages of a fully digital approach are demonstrated in this research including the simultaneous capture of numerous channels of information as well as increased flexibility in terms of data exploration across frequency, time, space, and noise domains. It is believed that G-Mode KPFM will be particularly useful for probing electrodynamics in photovoltaics, liquids, and ionic conductors.

More information about this research can be found in the journal Nanotechnology 27 105706.

Further reading

Kelvin probe microscopy measures chemical doping of reduced graphene oxide (Jun 2013)
Flat-packed unit brings creativity to AFM (Jan 2016)