This paper proposes a new method for optimization optics with a diffractive optical element (DOE) via a Hybrid
Taguchi Genetic Algorithm. A Diffractive Optical Element, based the theory of wave phase difference, takes advantage
of the negative Abbe number which might significantly eliminate the axial chromatic aberrations of optics. Following
the advanced technology applied to the micro lens and etching process, precisely-made micro DOEs can now be
manufactured in large numbers. However, traditional least damping square has its limitations for the optimization of
axial and chromatic aberrations with DOE. In this research, we adopted the genetic algorithm (GA) and incorporated the
steady Taguchi method into GA. Combining the two methods produced a new hybrid Taguchi-genetic algorithm
(HTGA). Suitable glass combinations and DOE positions were selected to minimize both axial and lateral chromatic
aberration in the optical system. This new method carries out the task of eliminating both axial and lateral chromatic
aberration, unlike DOE optimization by LDS, which works for axial aberration only and with less efficiency. Experiments show that the surface position of the DOE could be determined first; in addition, regardless of whether chromatic aberration was axial or longitudinal, issues concerning the optical lens's chromatic aberration could be significantly reduced, compared to results from the traditional least damping square (LDS) method.
Although optics is a common area of activity among professional physicists and engineers, the subject itself is typically not a significant component of Bachelor degrees in physics or engineering. Consequently, large numbers of scientists and engineers find themselves working in the field of optics without formal education in the subject. Although such education would often prove valuable to them, it is not conveniently available via conventional full-time courses. Another group of persons includes those who are not working in an optics-related field, but would like to be, and yet cannot contemplate the cost and dislocation associated with a conventional full-time Masters course. For both these groups, a flexible Masters course in optics by distance-learning could be appropriate. It is for these reasons that interest has arisen recently in such forms of optics education. This paper describes a flexible distance-learning model for postgraduate education in optics that has been implemented at the University of Reading, England, where there has been a full-time optics Masters course in Applied and Modern Optics for almost 40 years. The model is modular and credit-based, and includes various levels of qualification from CPD to Masters. A distance-learning module on optical design is discussed as an example, and it is hoped to make this module freely available on-line via the internet to delegates at this conference for them to explore in their own time. The importance of choosing optical-design case studies appropriate to this learning style is discussed. The problem of lab work within a distance-learning optics course is described, and current and possible future solutions are discussed.
The graded-response Hopfield neural network model has been used to solve the traveling salesman optimization problem. However, the mapping of an optical design optimization problem onto a neural net is more difficult. This paper describes how it can be done for the case of minimizing the chromatic aberration in a complicated twenty-element zoom-lens system by the selection of glass types. The problem is combinatorial in nature. It is suited to neural networks, and its solution is non-trivial by other means. Thus the use of neural networks to solve optical optimization problems is demonstrated.
The use of focal plane arrays (FPAs) in infrared imaging systems is becoming increasingly important. There are problems, however, in measuring their modulation transfer function (MTF) and their minimum resolvable temperature difference (MRTD) since these performance measures vary with the exposition of the image on the FPA. This limitation has been overcome through the introduction of a discrete MTF for these imaging systems using discrete Fourier transform techniques. This discrete MTF is a unique function of spatial frequency and has been measured using a microscanned discrete line spread function. It has also formed the basis of an objective MRTD, the results of which have been compared with subjective measurements.
Optical implementations of neural networks based on the Hopfield model have always found it difficult to produce the negative weights required for the interconnecting synaptic matrix. One solution involves the addition of a positive offset to the weights to ensure that they all become non-negative but this introduces another problem as a dynamic (or time-dependent) threshold value is then required which may be difficult to implement. The dynamic threshold arises out of an inconsistency in the implementation. To overcome this our implementation employs a biased (non-negative) interconnection matrix which is dynamically multiplied by a diagonal matrix version of the neural state vector so that the same biasing is experienced. The above problem then no longer arises and we are left with a static threshold value. The method is demonstrated in an optoelectronic system employing 50 fully interconnected neurons. This uses a laser source for the neurons a computer driven liquid crystal spatial light modulator to produce the interconnection weights and a photodiode array with appropriate electronic circuitry to introduced the summing and thresholding aspect. 1. .