Data processing: generic control systems or specific application – Specific application – apparatus or process – Product assembly or manufacturing
Reexamination Certificate
2001-06-26
2004-01-13
Gandhi, Jayprakash N. (Department: 2126)
Data processing: generic control systems or specific application
Specific application, apparatus or process
Product assembly or manufacturing
C700S110000, C700S121000, C029S025010
Reexamination Certificate
active
06678570
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates generally to semiconductor device manufacturing and, more particularly, to a method and apparatus for determining output characteristics using tool state data.
2. Description of the Related Art
There is a constant drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a group of wafers, sometimes referred to as a “lot,” using a variety of processing tools, including photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal processing tools, implantation tools, etc. The technologies underlying semiconductor processing tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the processing tools that are currently commercially available suffer certain deficiencies. In particular, such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender non-optimal control of critical processing parameters, such as throughput, accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
One technique for improving the operation of a semiconductor processing line includes using a factory wide control system to automatically control the operation of the various processing tools. The manufacturing tools communicate with a manufacturing framework or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The equipment interface is connected to a machine interface which facilitates communications between the manufacturing tool and the manufacturing framework. The machine interface can generally be part of an advanced process control (APC) system. The APC system initiates a control script based upon a manufacturing model, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. Often, semiconductor devices are staged through multiple manufacturing tools for multiple processes, generating data relating to the quality of the processed semiconductor devices.
During the fabrication process various events may take place that affect the performance of the devices being fabricated. That is, variations in the fabrication process steps result in device performance variations. Factors, such as feature critical dimensions, doping levels, contact resistance, particle contamination, etc., all may potentially affect the end performance of the device. Various tools in the processing line are controlled in accordance with performance models to reduce processing variation. Commonly controlled tools include photolithography steppers, polishing tools, etching tools, and deposition tools. Pre-processing and/or post-processing metrology data is supplied to process controllers for the tools. Operating recipe parameters, such as processing time, are calculated by the process controllers based on the performance model and the metrology information to attempt to achieve post-processing results as close to a target value as possible. Reducing variation in this manner leads to increased throughput, reduced cost, higher device performance, etc., all of which equate to increased profitability.
In some cases, a particular tool may not be well suited for automatic process control, because it is difficult to measure an output characteristic of the processed wafer to provide feedback for controlling the operating recipe. For example, copper is being used increasingly to form electrical interconnect structures in semiconductor devices, such as microprocessors. Copper has advantages over previously used interconnect materials, such as aluminum, and its use has allowed the production of higher density, lower power devices than were previously feasible. One problem area in a copper fabrication environment is the difficulty in collecting in-line metrology data related to the copper processes.
Because copper is not readily etched by chemical means, copper interconnect structures are typically formed by forming a trench in an insulating layer, electroplating a copper layer to fill the trench, and polishing the copper to remove the portion extending beyond the trench. Typical metrology techniques for measuring the outputs of the copper processing steps are destructive. For example, many tests require a cross-sectional analysis, which destroys the device or requires the use of expensive test wafers. Because, of the destructive nature of the metrology, the amount of feedback generated is not sufficient to reliably implement run to run control techniques for the copper processes.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.
SUMMARY OF THE INVENTION
One aspect of the present invention is seen in a method for determining output characteristics of a workpiece. The method includes generating a tool state trace related to the processing of a workpiece in a tool; comparing the generated tool state trace to a library of reference tool state traces, each reference tool state trace having an output characteristic metric; selecting a reference tool state trace closest to the generated tool state trace; and determining an output characteristic of the workpiece based on the output characteristic metric associated with the selected reference tool state trace.
Another aspect of the present invention is seen in a manufacturing system including a tool and a tool state monitor. The tool is adapted to process a workpiece. The tool state monitor is adapted to generate a tool state trace related to the processing of a workpiece in the tool, compare the generated tool state trace to a library of reference tool state traces, each reference tool state trace having an output characteristic metric, select a reference tool state trace closest to the generated tool state trace, and determine an output characteristic of the workpiece based on the output characteristic metric associated with the selected reference tool state trace.
REFERENCES:
patent: 5526293 (1996-06-01), Mozumder et al.
patent: 6240329 (2001-05-01), Sun
patent: 6356858 (2002-03-01), Malka et al.
patent: 6505090 (2003-01-01), Harakawa
Pasadyn Alexander J.
Sonderman Thomas J.
Advanced Micro Devices , Inc.
Gandhi Jayprakash N.
Williams Morgan & Amerson P.C.
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