1. Field of the Invention
The present invention relates to an adaptive-learning type product-sum operation circuit element, an adaptive-learning type product-sum operation circuit and an adaptive-learning type product-sum operation circuit array for weighing and adding a number of signals input into a plurality of neuron circuits in a neural network.
2. Related Art Statement
As a method of giving a weight function to the input portion of a neuron circuit, i.e., synaptic connection, there are two methods of giving a value obtained by software simulation as a certain value and of determining the optimum value by learning with a teacher after the construction of a network.
Both methods have a problem of enormously increasing learning time in a practical network having more than 10,000 neurons.
The present invention is to obviate the aforesaid shortcoming. The present invention relates to a circuit for making the weight of synaptic connection the optimum by adaptive-learning with the aid of residual polarization of a ferroelectric film, and when this circuit is used, even if the use of a network is started under such a condition that the weight of synaptic connection is not the optimum, a function of making the weight of synaptic connection the optimum as the use is progressed can be expected. A specific circuit constructs a matrix of synaptic connection of m.times.n by connecting in parallel adaptive-learning type MISFET (metal-insulator-semiconductor field effect transistors) with the use of a ferroelectric thin film as a gate insulating film by m stages equal to a number of input signals to form synaptic connection of one neuron circuit, and further aligning the MISFETs for other neuron circuits so that n rows are formed in total.