AN ARCHITECTURE FOR FULLY INTEGRATED LARGE SCALE NEURAL NETWORKS

Abstract

To apply neural networks to many engineering applications, large networks will be required. Such networks are difficult to build using standard crystalline silicon technology due to limitations in both the fabrication and packaging processes. An architecture is proposed where amorphous silicon photoresistors are used to store the synaptic weights. A single plate of amorphous silicon is able to contain up to 100 million photoresistors, exploiting readily available fabrication technology. Using an external light source, each photoresistor can be individually adjusted allowing them to be configured as programmable fixed-value resistors. The processing compatibility of polysilicon and amorphous silicon allows the same glass substrate to be used for large-area integration of the photosensors, the analogue neural network and the neurons. The integration of the photosensors and the rest of the network may be used to alleviate the interface problem at the inputs resulting in a design with a very simple architecture that is both elegant and simple to fabricate. This paper describes such a design in which amorphous silicon technology is applied to neural network hardware.