Implementation of neural networks using quantum well based excitonic devices-device requirement studies

The authors examine experimentally and theoretically two devices based on III-V technology, which are critical in the implementation of the Hopfield model as well as other neural type networks for associative memories. The devices are based on Stark effect of excitonic transitions. P-i (multiquantum wells)-n structures using GaAs/AlGaAs provide a controller-modulator device which has the integrating-thresholding properties required of neurons. The p-i-n structures also provide programmable modulators which can serve as a synaptic mask. Using Monte Carlo techniques, the authors examine an all-optical architecture to implement the Hopfield network. No external feedback-thresholding circuitry is required in this implementation due to special design of the controller-modulator device. Speed and stability issues of this architecture are also addressed. The computer simulation results provide insight into how the controller-modulator device should be improved for better network implementation. The basic technology now exists for such an implementation.<>