Abstract: A data visualization method and apparatus permit the representation of multi-dimensional input data sequences as output sequences of closed form visual representations known as blobs. By noting changes in the shape of blobs as processing of the input sequence progresses, changes in characteristics of entities represented by the input sequence can be readily ascertained by observation. In an illustrative embodiment, a plasma etching process used in semiconductor fabrication can be observed and controlled by monitoring changes in blobs corresponding to the etching process variables.

Abstract: A method for determining the endpoint of a plasma etch process is disclosed. The endpoint of the plasma etch process is determined using an acoustic cell attached to an exhaust port on a reaction chamber of a plasma reactor. At least a portion of the gas from the reaction chamber flows into the acoustic cell during the plasma etch process. Acoustic signals are periodically transmitted through the gas flowing in the acoustic cell and a first velocity for the acoustic signals associated with etching a first material layer formed on a substrate is determined. Thereafter, the endpoint of the plasma etch step is determined when the first velocity changes to a second velocity associated with etching the first material layer through its thickness to its interface with an underlying material layer. The gas from the reaction chamber optionally flows through a compressor prior to flowing into the acoustic cell. The compressor increases the pressure of the gas that flows into the acoustic cell.

Abstract: The present invention is predicated upon the fact that an emission trace from a plasma glow used in fabricating integrated circuits contains information about phenoma which cause variations in the fabrication process such as age of the plasma reactor, densities of the wafers exposed to the plasma, chemistry of the plasma, and concentration of the remaining material. In accordance with the present invention, a method for using neural networks to determine plasma etch end-point times in an integrated circuit fabrication process is disclosed. The end-point time is based on in-situ monitoring of the optical emission trace. The back-propagation method is used to train the network. More generally, a neural network can be used to regulate control variables and materials in a manufacturing process to yield an output product with desired quality attributes. An identified process signature which reflects the relation between the quality attribute and the process may be used to train the neural network.

Abstract: The present invention is predicated upon the fact that a process signature from a plasma process used in fabricating integrated circuits contains information about phenomena which cause variations in the fabrication process such as age of the plasma reactor, densities of the wafers exposed to the plasma, chemistry of the plasma, and concentration of the remaining material. In accordance with the present invention, a method for using neural networks to determine plasma etch end-point times in an integrated circuit fabrication process is disclosed. The end-point time is based on in-situ monitoring of at least two parameters during the plasma etch process. After the neural network is trained to associate a certain condition or set of conditions with the endpoint of the process, the neural network is used to control the process.