The second generation of computer-controlled scanning electron microscopes (SEMs), i.e. instruments directed by software executing on conventional personal computers (PC-SEMs), has become the industry standard. Although limited automation has been incorporated by most manufacturers, these features do not significantly alleviate the requirement for operator expertise to attain optimal performance from the microscope. Our approach to the expertise deficit has been to pursue a knowledge-based approach to microscope control, encapsulating human knowledge in software to achieve expert performance. The task of SEM operation was decomposed into five phases - Specimen Preparation, SEM Preparation, SEM Initialisation, SEM Optimisation and External Analysis - and the central three phases were modelled in detail. A stand-alone prototype system (XpertEze) was developed to utilise this model for the LEO 400 series. A web-based version was implemented to support intelligent remote microscopy.
A stand-alone knowledge-based system has several disadvantages: its user interface competes for screen space with the conventional control software, its speed of execution is reduced as its instructions must be transferred via application programming interfaces to the microscope, and the operator must learn and manage two sets of software. Our ongoing work has been to remove these problems by incorporating the XpertEze system directly within the control system.