Data processing: generic control systems or specific application – Generic control system – apparatus or process – Optimization or adaptive control
Reexamination Certificate
2002-04-03
2004-06-01
Pert, Evan (Department: 2829)
Data processing: generic control systems or specific application
Generic control system, apparatus or process
Optimization or adaptive control
C700S121000, C438S005000
Reexamination Certificate
active
06745086
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates generally to the field of semiconductor device manufacturing and, more particularly, to a method and apparatus for method and apparatus for determining control actions incorporating defectivity effects.
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 lot of wafers using a variety of process tools, including photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal process tools, implantation tools, etc. The technologies underlying semiconductor process 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 process 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 process 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 that 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.
Target values for the various processes performed are generally based on design values for the devices being fabricated. For example, a particular process layer may have a target thickness. Operating recipes for deposition tools and/or polishing tools may be automatically controlled to reduce variation about the target thickness. In another example, the critical dimensions of a transistor gate electrode may have an associated target value. The operating recipes of photolithography tools and/or etch tools may be automatically controlled to achieve the target critical dimensions.
In some cases, the goal to achieve target values may competes with the simultaneous goal of minimizing defects in the fabricated devices. Defects can take the form of particle contamination, missing or extra patterns, electrical defects, feature damage (e.g., from overpolishing), electrical faults, etc. Different processes performed during the fabrication of devices, by nature, have different propensities for inducing defects in the processed devices. A process control variable that is modified to achieve a target value goal may also effect the propensity of the process to induce defects. For example, in a chemical mechanical planarization process, process variables, such as polishing pressure, polish time, polishing rotation speed, etc. may be controlled automatically to achieve desired process layer removal rates and/or wafer surface uniformity characteristics. However, changing the process control variables also has the effect of changing the defectivity characteristics of the process. For example, as polishing pressure or time increases, the likelihood of damaging the features underlying the polished layer increases. If an underpolishing condition exists, remnants of the process layer may be still present in undesirable locations.
FIG. 1A
illustrates a cross-section of an exemplary semiconductor device
100
that is subjected to a planarization process. Process variables associated with the planarization process may be controlled to achieve desired post-planarization targets. The semiconductor device
100
includes a base layer
110
with a plurality of trenches
120
defined therein. For example, the base layer
110
may be a dielectric layer (e.g., dielectric constant less than 5.0), such as silicon dioxide, and the trenches
120
may be used to form copper interconnect lines. In another example, the base layer
110
may be a substrate layer (e.g., an epitaxial silicon layer formed over a bulk silicon substrate), and the trenches
120
may be used to form shallow trench isolation (STI) structures between active regions of subsequently formed devices (e.g., transistors). A process layer
130
(e.g., copper for an interconnect line and silicon dioxide for an STI structure) is formed over the base layer to fill the trenches
120
. One or more intermediate layers, such as a stop layer (not shown) may be formed between the base layer
110
and the process layer
130
.
As seen in
FIG. 1B
, the process layer
130
is polished to remove the portions not disposed within the trenches
120
. Various endpoint techniques may be used for determining the end point for the polishing process, however, these endpoint detection techniques are approximate, and some polishing may continue after the process layer
130
has been cleared.
The chemical slurry used in the polishing process typically has a higher etch rate for the
Bode Christopher A.
Pasadyn Alexander J.
Sonderman Thomas J.
Advanced Micro Devices , Inc.
Pert Evan
Williams Morgan & Amerson P.C.
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