BACKGROUND

The present invention generally relates to resistive processing units and, more generally, to resistive processing units based on a floating gate architecture.

An artificial neural network (ANN) is an information processing system that is inspired by biological nervous systems, such as the brain. The key element of ANNs is the structure of the information processing system, which includes a large number of highly interconnected processing elements (called “neurons”) working in parallel to solve specific problems. ANNs are furthermore trained in-use, with learning that involves adjustments to weights that exist between the neurons. An ANN is configured for a specific application, such as pattern recognition or data classification, through such a learning process.

Referring now toFIG. 1, a generalized diagram of a neural network is shown. ANNs demonstrate an ability to derive meaning from complicated or imprecise data and can be used to extract patterns and detect trends that are too complex to be detected by humans or other computer-based systems. The structure of a neural network is known generally to have input neurons102that provide information to one or more “hidden” neurons104. Connections108between the input neurons102and hidden neurons104are weighted and these weighted inputs are then processed by the hidden neurons104according to some function in the hidden neurons104, with weighted connections108between the layers. There can be any number of layers of hidden neurons104, and as well as neurons that perform different functions. There exist different neural network structures as well, such as convolutional neural network, maxout network, etc. Finally, a set of output neurons106accepts and processes weighted input from the last set of hidden neurons104.

This represents a “feed-forward” computation, where information propagates from input neurons102to the output neurons106. Upon completion of a feed-forward computation, the output is compared to a desired output available from training data. The error relative to the training data is then processed in “feed-back” computation, where the hidden neurons104and input neurons102receive information regarding the error propagating backward from the output neurons106. Once the backward error propagation has been completed, weight updates are performed, with the weighted connections108being updated to account for the received error. This represents just one variety of ANN.

While ANNs can be implemented in software, hardware implementations can be much more efficient, as the state of the weighted connections108is represented by physical properties of the network.

SUMMARY

A method of forming a semiconductor device includes forming a drain/gate contact, in an opening of a layer of dielectric material, that includes a portion that extends up along sidewalls of the opening. A drain layer is formed on a bottom surface of the drain/gate contact. A trapped insulator layer is formed on sidewalls of the drain/gate contact. A channel layer is formed in the opening. A source layer is formed on the channel layer.

A method of forming a semiconductor device includes forming a layer of dielectric material. An opening is etched in the layer of dielectric material. A drain/gate contact is formed in the opening and in contact with an underlying conductive contact formed in a substrate. The drain/gate contact includes a portion that extends up along sidewalls of the opening. A drain layer is formed on a bottom surface of the drain/gate contact. A gate dielectric layer is formed on the sidewalls of the drain/gate contact. A trapped insulator layer is formed on sidewalls of the gate dielectric layer. A channel layer is formed in the opening. A source layer is formed on the channel layer. A top conductive contact is formed on the source layer.

A semiconductor device includes a gate/drain contact, formed in an opening of a layer of dielectric material, that includes a portion that extends up along sidewalls of the opening. A drain layer is formed on a bottom surface of the drain/gate contact. A trapped insulator layer is formed on sidewalls of the drain/gate contact. A channel is formed on the drain layer. A source layer is formed on the channel.

These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.

