Organics have emerged as one of the most rapidly developing research areas for improved electro-optical materials. Many reviews have been written on this subject following progress from the early physical measurements from single crystals through the subsequent creation of linear electro-optical polymers. Applications of polymers have produced promising results and extensive development activities are in progress at industrial and academic laboratories to demonstrate device applications. Most of this activity concerns waveguide devices which are very appropriate device structures for the new E/O polymers. More complex devices such as spatial light modulators have not yet been developed but are an attractive application area. The purpose of this paper is to introduce organic nonlinear optical polymers to the spatial light modulator community and highlight some of the advantages and shortcomings of the current materials in this device format.
Electric field effects produced by coupling with the dielectric anisotropy, ferroelectric polarization, flexoelectric polarization and the induced polarization (electroclinic effect) are described. The use of these effects in electrooptic devives is also discussed.
The magneto-optic spatial light modulator (MOSLM), commercially available through Semetex Corporation, has successfully been integrated into a variety of optical signal processing, optical pattern recognition, and optical computing applications. This paper reports improved device performance characteristics for the rotation angle, absorption coefficient, on-state transmission, and contrast ratio (68,000/1) for two formulations of the material, GGG and LLC. Device considerations for two dimensional arrays of pixels are covered including new measurements on high speed frame rate operation (up to 2000 frames/sec) and shock and vibration testing on the array. Finally, we will indicate some of the potential for this technology.
This paper discusses the operating characteristics of a new class of spatial light modulators that utilize ferroelectric liquid crystals, and their application to building optoelectronic computing architectures.
This paper covers recent theoretical and experimental work in the area of multiple quantum well modulators with particular emphasis on optical processing applications. The paper covers the analysis of MQW as a modulator element as well as one- and two-dimensional spatial light modulator structures, both optically and electrically addressed. Recent experimental efforts in developing MQW modulators structures are surveyed. Finally, future development such as quantum dots as well as the relative role of MQW within the family of spatial light modulators is discussed.
A brief overview of deformable-mirror spatial light modulator (SLM) technology is presented, followed by the first comprehensive review of the Texas Instruments deformable-mirror device (DMD), a subclass of deformable-mirror SLMs. The DMD architecture, fabrication, and operation (both analog and digital) are described. It is shown that amplitude-dominant or phase-dominant modulation is obtained by a simple modification of the pixel architecture. Representative DMD applications are presented.
The design of high performance computers such as array processors has reached a critical stage. These machines are increasingly using parallel processing methods to achieve higher performance. They require low cost memory systems with much higher capacities and bandwidths than available today while retaining small volume, weight, and power consumption characteristics. Massively interconnected optical computers will require even higher performance memory systems. This paper reviews various 3-D memory concepts that are proposed to meet these demands.
An introduction to photorefractive devices is provided, which compares the basic operation, fundamental functional capabilities, and performances of these devices. Applications of photorefractive devices for analog and digital parallel optical computing architectures and systems are discussed; their performance characteristics are evaluated with respect to theoretical and practical limitations and advances of current technology.
The study of complex multidimensional nonlinear dynamical systems and the modeling and emulation of cognitive brain-like processing of sensory information (neural network research), including the study of chaos and its role in such systems would benefit immensely from the development of a new generation of programmable analog computers capable of carrying out collective, nonlinear and iterative computations at very high speed. The massive interconnectivity and nonlinearity needed in such analog computing structures indicate that a mix of optics and electronics mediated by judicial choice of device physics offer benefits for realizing networks with the following desirable properties: (a) large scale nets, i.e. nets with high number of decision making elements (neurons), (b) modifiable structure, i.e. ability to partition the net into any desired number of layers of prescribed size (number of neurons per layer) with any prescribed pattern of communications between them (e.g. feed forward or feedback (recurrent)), (c) programmable and/or adaptive connectivity weights between the neurons for self-organization and learning, (d) both synchroneous or asynchroneous update rules be possible, (e) high speed update i.e. neurons with lisec response time to enable rapid iteration and convergence, (f) can be used in the study and evaluation of a variety of adaptive learning algorithms, (g) can be used in rapid solution by fast simulated annealing of complex optimization problems of the kind encountered in adaptive learning, pattern recognition, and image processing. The aim of this paper is to describe recent efforts and progress made towards achieving these desirable attributes in analog photonic (optoelectronic and/or electron optical) hardware that utilizes primarily incoherent light. A specific example, hardware implementation of a stochastic Boltzmann learning machine, is used as vehicle for identifying generic issues and clarify research and development areas for further advancement of the field, in particular the development of architectures and methodologies for learning in self-organizing networks that employ a new type of quasi-nonvolatile storage medium: electron trapping material.
A Lyapunov or "energy" function based on Kosko's BAM model of associative memory is derived for optical associative memories based on thin holograms in a nonlinear cavity. The dynamic behavior is illustrated using computer simulations.
A generic architecture for realizing neural networks is presented in which the synaptic interaction matrix is loaded in parallel into an electronic integrated circuit from a SLM. Three types of the electronic processors are described using CCD, CID and CMOS technologies respectively. The pros and cons of currently existing SLMs for this architecture are pointed out.
Several implementations of neural network models by using coherent optics are described, with emphasis on the advantages of coherent optics. Furthermore, by using the recently developed surface emitting micro-laser diode array, the systems can be made compact and robust, still preserving the power of coherent optics.