An automatic focusing and stigmating system for the SEM

A digital minicomputer has been interfaced with a scanning electron microscope, and programmed to control the excitations of the objective lens and the stigmator of the microscope. The electron beam is scanned by a digital scan generator and the digitised video signal is used for computations. To focus the microscope, a parameter related to the 'sharpness' of the image is maximised, and to set the stigmator, the directional information in the above- and below-focus images is used. | A digital minicomputer has been interfaced with a scanning electron microscope, and programmed to control the excitations of the objective lens and the stigmator of the microscope. The electron beam is scanned by a digital scan generator and the digitized video signal is used for computations. To focus the microscope, a parameter related to the 'sharpness' of the image is maximized, and to set the stigmator, the directional information in the above and below-focus images is used.

Topographical analysis in the SEM using an automatic focusing technique

A novel technique for automated topographical analysis in the SEM has been investigated. It utilizes a 16-bit minicomputer arranged to act as an automatic focusing unit. The computer is coupled to the objective lens of the microscope, by means of a digital to analogue converter, and may regulate the excitation of the lens under program control. Further digital-to-analogue converters allow the computer to act as a programmable scan generator by applying ramp waveforms to the scan amplifiers, permitting the beam to be swept over a small sub-region of the field of interest. The video signal is sampled and applied to an analogue-to-digital converter; the resultant binary numbers are stored in computer memory as an array of values representing relative image intensities within a subregion. A differencing algorithm applied to the collected data allows the level of objective lens excitation to be found at which the sharpness of the image is optimized, and the excitation may be related to the working distance for that subregion through a previous calibration experiment. The sensitivity of the method for detecting small height changes is theoretically of the order of 1 μm.

BOLTZMANN MACHINES FOR SPEECH RECOGNITION.

Boltzmann machines offer a new and exciting approach to automatic speech recognition, and provide a rigorous mathematical formalism for parallel computing arrays. In this paper we briefly summarize Boltzmann machine theory, and present results showing their ability to recognize both static and time-varying speech patterns. A machine with 2000 units was able to distinguish between the 11 steady-state vowels in English with an accuracy of 85%. The stability of the learning algorithm and methods of preprocessing and coding speech data before feeding it to the machine are also discussed. A new type of unit called a carry input unit, which involves a type of state-feedback, was developed for the processing of time-varying patterns and this was tested on a few short sentences. Use is made of the implications of recent work into associative memory, and the modelling of neural arrays to suggest a good configuration of Boltzmann machines for this sort of pattern recognition.

MPC: A paradigm shift for automatic control

Model Predictive Control (MPC) represents a major paradigm shift in the field of automatic control. This radically affects synthesis techniques (illustrated by control of an unstable system) and underlying concepts (illustrated by control of a multivariable system), as well as lifting the control engineer's focus from prescriptions to specifications ('what' not 'how', illustrated by emulation of a conventional autopilot). Part of the objective of this paper is to emphasize the significance of this paradigm shift. Another part is to consider the fact that this shift was missed for many years by the academic community, and what this tells us about teaching and research in the field.