Neural network for determining the endpoint in a process

Abstract

There is provided a system and method for pattern recognition of an endpoint curve for a dry etch process. The system trains a neural network with a group of training curves corresponding to the dry etch process, wherein the training curves contain normal and abnormal features. The system receives an endpoint curve at the neural network representing a dry etch process and detects an abnormal feature in the endpoint curve.

Description

DESCRIPTION OF THE INVENTION[0001] 1. Field of the Invention[0002] This invention relates to endpoint detection in a semiconductor process utilizing a neural network.[0003] 2. Background of the Invention[0004] Today in semiconductor processing, different structures, such as trenches and vias in semiconductor devices are formed by etching processes. FIGS. 1A-1C illustrate the steps in a known process for etching a semiconductor device. As shown in FIG. 1A, a semiconductor device 100 comprises a silicon substrate 102, a polysilicon layer 104, and an insulating layer 106. On insulating layer 106 is deposited a photoresist layer 108. Photoresist layer 108 is patterned into a predetermined structure. The predetermined structure may be, for example, a via hole or trench. Photoresist layer 108 serves as a mask for the etching process.[0005] Then, as shown in FIG. 1B, photoresist layer 108 functions as a mask during the etch process. In the etching process, insulating layer 106 is etched un...

AI Technical Summary

Benefits of technology

[0014] In certain aspects consistent with the present invention, there is provided a system and method for determining an endpoint of an etching process by utilizing a neural network. By learning the features of a group of endpoint curves containing normal and abnormal features for an etch process, the neural network may determine the endpoint of the etch process through pattern recognition.

[0015] In one aspect consistent with the present invention, there is provided a system and method for pattern recognition of an endpoint curve for a dry etch process. The system trains a neural network with a group of training curves corresponding to the dry etch process, wherein the training curves contain normal and abnormal features. The system receives an endpoint curve at the neural network representing a dry etch process and detects an abnormal feature in the endpoint curve.

Problems solved by technology

If etching is continued after the etched target is completely removed, the underlying material may be excessively etched, or the etching shape may be undesirably changed.

Oftentimes, it is difficult to determine the exact endpoint of an etching process.

Such is the case when an endpoint curve contains abnormal features that may be hard to detect by conventional optical spectroscopy.

However, in the case of overetching, the underlying materials may be damaged.

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