National Physical Laboratory

High dynamic range surface metrology

An ever increasing number of advanced manufacturing tasks demand fast inspection of large area surfaces to high resolution to enhance device function, improve lifetime and reduce costly scrap. Current measurement techniques are either too slow or have insufficient resolution for the required applications. NPL has begun a long-term multi-strand project to develop a toolbox of techniques to address this vital measurement need.

Key applications and beneficiaries include:

  • Photovoltaics (energy)
  • Micro-optics (energy, environment, consumer electronics)
  • Protective coating integrity (environment, consumer items)
  • Roll-to-roll devices (multiple applications)

Working in collaboration with key industry and academic partners, NPL is currently pursuing the following strands of work:

  • Sensor benchmarking
  • Advanced inline sensor development
  • Techniques for resolution enhancement
  • Intelligent sampling and multi-sensor data fusion

This project is supported in part by the FP7 project NanoMend.

NPL is seeing increasing need for surface metrology that achieves a greater and greater sample area throughput - is faster and faster - whilst maintaining or improving measurement resolution where it matters. This is not just incremental progress - industry needs a step change in capability in this area.

The ideal capability would combine the throughput of today's macro-scale freeform metrology with the resolution performance of precision laboratory microscopes. In addition, the metrology may need to cope with features on non-planar or moving substrates, withstand the difficulties of industrial environments, maintain reliability in production and not interact with surrounding processes, and of course meet the production engineer's budget.

High dynamic range ideal

The solution developed by the metrology community is something like this. The measurement task must be simplified as much as possible, based on prior knowledge about the application and requirements. Typically, only a small fraction of the surface actually needs to be measured. Then, intelligent sampling techniques should be used to control a hybrid, advanced sensor system selected for the application. The output data must be fused with the existing data and fed back into the working knowledge and sampling control. Resolution enhancement techniques allow more surface information to be deduced. Finally, the information must be distilled to a simple, reliable user output for process input.

Research themes include:

  • Advanced inline sensor development to significantly extend current capabilities on measurement throughput, range and resolution in the production environment.
  • Calibration protocols and artefacts for in-process defect-detecting optical metrology, developing existing good practice to account for operational constraints.
  • Advanced techniques to enhance the resolution of optical sensors by intelligently recovering lost information about the surface. The primary focus of this work is to develop methods that maximally exploit this a priori knowledge of the object, in order to produce images that display a resolution beyond the conventional diffraction limit.
  • Intelligent sampling and multi-sensor data fusion: non-standard methods for sampling and handling of data, such as online measurement strategy adaptation based on gathered data.
Nanomend logo

FP7 NanoMend

This 7.25 M€, four-year, 14 partner FP7 collaboration will develop technologies that are able to detect and correct micro and nano-scale defects in thin films, without slowing production speed - improving product performance, yield and lifetime. The project focuses on two important example applications: flexible solar cells, and paperboard food packaging.

NPL's key contributions are in the development of novel sensing techniques, including resolution enhancement, traceable water vapour transmission rate (WVTR) metrology, and sensor benchmarking and evaluation.

For more information on this project, please visit

Research team

  • Chris Jones
  • Daniel O'Connor
  • Jeremy Coupland (visiting professor from Loughborough University)
  • Adam Krysiński (EngD student in association with the University of Strathclyde)
  • Giuseppe Moschetti (PhD student in association with the University of Huddersfield)

Recent publications


For more information, please contact Daniel O'Connor or Christopher Jones

Last Updated: 10 Dec 2014
Created: 5 Apr 2013


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