Advanced process control optimization

A method for automatic process control (APC) performance monitoring may include, but is not limited to: computing one or more APC performance indicators for one or more production lots of semiconductor devices; and displaying a mapping of the one or more APC performance indicators to the one or more production lots of semiconductor devices.

BACKGROUND

Advanced process control (APC) systems are presently used to predict process corrections for photolithographic devices (e.g. steppers, scanners and the like) by calculating overlay model parameters from various production lots of semiconductor devices. However, current methodologies for determining the performance of such APC systems may merely include analysis of such production lots. Using such methods may result in the consideration of errors introduced independently of the APC algorithms (e.g. tool errors, metrology errors, and the like). As such, an accurate measure of APC performance may be difficult to obtain from simple final production lot analysis. As such, a need exists to determine the relative performance of APC independent from other process variables.

SUMMARY OF THE INVENTION

A method for automatic process control (APC) performance monitoring may include, but is not limited to: computing one or more APC performance indicators for one or more production lots of semiconductor devices; and displaying a mapping of the one or more APC performance indicators to the one or more production lots of semiconductor devices.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention generally relates to systems and methods for automatic process control (APC) of photolithographic systems. More particularly, the present invention relates to systems and methods for determining the performance characteristics of an APC system, monitoring the predicted process model parameters generated by the APC system, and optimizing one or more process control parameters.

Referring toFIG. 1, a photolithographic device fabrication system100is shown. The photolithographic device fabrication system100may include a photolithographic fabrication device101. The photolithographic fabrication device101may include a stepper or scanner-type photolithographic device configured to dispose one or more process layers on a substrate to generate a semiconductor device102.

The photolithographic device fabrication system100may further include a metrology system103. A semiconductor device102fabricated by the photolithographic fabrication device101may be provided to a metrology system103. The metrology system103may perform one or more metrological inspections (e.g. process layer overlay error determination) to determine the quality of the photolithographic processes employed by the photolithographic fabrication device101. The metrology system103may generate metrology data104(e.g. overlay error values) indicative of the metrological inspection of the semiconductor device102.

The photolithographic device fabrication system100may further include an APC system105. The APC system105may monitor the performance of the photolithographic fabrication device101and automatically adjust one or more process parameters (e.g. translation, rotation, magnification, dose, focus, and the like) to optimized the performance of the photolithographic fabrication device101. Specifically, the APC system105may receive metrology data104from the metrology system103, analyze the performance of the photolithographic fabrication device101as evidenced by the metrology data104, and provide one or more process control parameters106to the photolithographic fabrication device101.

Current APC systems may calculate overlay model parameters for use in configuring photolithographic devices based on analysis of production lots of semiconductor devices. Using such systems may result in the consideration of error contributions that are independent of the APC algorithms (e.g. internal fabrication tool errors, metrology errors, and the like). As such, such systems may fail to provide an accurate isolated view of the performance of the APC systems.

In order to address such deficiencies, the APC system105of the photolithographic device fabrication system100may include an APC monitoring system107. The APC monitoring system107may monitor the metrology data104from the metrology system103to determine the impact that the process modeling and estimation operations of the APC system105have on photolithographic process performance. Specifically, it may be the case that the APC monitoring system107may compute an APC performance indicator. The APC performance indicator may be a measure of impact that the performance of the modeling and estimation operations of the APC system105have on the semiconductor devices102fabricated according to those modeling and estimation operations. For example, the APC monitoring system107may receive historical data regarding overlay error data detected by the metrology system103. This historical data may be correlated with the computed APC process control parameters106generated by the APC system105and provided to the photolithographic fabrication device101in order to fabricate the semiconductor device102having the metrology data104.

For example, an APC performance indicator may be computed by an APC monitoring system107. An APC performance indicator may include an absolute value of the difference between raw overlay error data associated with a production lot and a residual overlay value (e.g. the overlay portion remaining following model fitting) computed from an overlay control model by the APC system105in determining the process control parameters to be provided to the photolithographic fabrication device101. Specifically, if a linear control model is employed, a linear residual may be employed.

Still further, a combined APC performance indicator may take into account both the residuals and the overlay specification (e.g. a user defined allowable overlay margin for a given device). For example,FIG. 2illustrates various metrics representative of the performance characteristics of three photolithographic processes A, B and C.

The first row of charts ofFIG. 2illustrates the raw data representative of the overlay errors detected across various production lots. The second row of charts ofFIG. 2illustrates the residual computed for various production lots.

The third row of charts ofFIG. 2illustrates the inventive APC performance indicator. As shown inFIG. 2, the APC performance indicator may be computed as the absolute value of the difference between the raw overlay error data and the computed residual data. For example, for lots 1-8 of Process A, the APC performance indicators are 1, 1, 1, 2, 2, 1, 1 and 1, respectively. For lots 1-8 of Process B, the APC performance indicators are 1, 1, 1, 2, 2, 1, 1 and 1, respectively. For lots 1-8 of Process C, the APC indicators are 3, 2, 3, 3, 2, 1, 2 and 3, respectively. As can be seen, the APC indicators for Process C indicate poor APC performance for that process (e.g. wide variation and greater APC indicator values).

Additionally, the third row of charts ofFIG. 2illustrates the percentage overlay spec.

The APC performance indicator methods described above may be extended to the various photolithographic technologies (e.g. 22 nm technologies, 28 nm technologies, 32 nm technologies, and the like), devices (e.g. DRAM, Flash, MPU and the like) and process layers of a photolithographic process for a given semiconductor process. For example, as shown inFIG. 3A, a graphical user interface300may be provided (e.g. displayed on a display device) by the APC monitoring system107. As shown inFIG. 3A, varying APC performance indicator levels may be accorded specific colors within a spectrum. For example, APC performance indicator values between 0 and 1 nm may be green, APC performance indicator values between 1 and 2 nm may be light blue, APC performance indicator values between 2 and 3 nm may be yellow, APC performance indicator values between 3 and 4 nm may be dark blue and APC performance indicator values greater than 4 nm may be red.

FIG. 3Billustrates a plot of the APC performance indicator values for a given photolithographic technology (e.g. 32 nm technology) running on a given photolithographic device (e.g. device “B”) for a given process layer (e.g. Process layer “n”) over a series of production lots. As shown inFIG. 3B, the maximum value of the APC performance indicator over the subject production lots is 3. As such, in the graphical user interface300ofFIG. 3A, the field associated with 32 nm photolithographic technology on device “B” for process layer “n” is shown in dark blue.

Accordingly, the graphical user interface300may present a comprehensive map of APC performance for all photolithographic device fabrication systems101. Such a map may permit a user to view trends in APC performance data and take corrective action. For example, as shown inFIG. 3, all fields associated with process layers “k” and “m” indicate poor APC performance, regardless of device (e.g. a device independent performance issue). Alternately, the percentage view (e.g. APC prediction error/overlay specification value) associated with layer “k” shows varying levels of APC performance across multiple devices.

Upon determination of a poor APC performance as described above, it may be desirable account for correlations between various fabrication process control parameters such that the impact of those parameters on APC performance may be determined and adjusted to optimize APC performance.

Traditional methods for monitoring high-order error response signatures on wafers involve monitoring individual control model parameters. However, the simultaneous monitoring of multiple parameters may become impractical. For example, the 3rd-order grid model may involve 20 control model parameters.

Instead, the quantity (e.g. mean+3sigma of a modeled parameter) of the model may be monitored. For example, a second-order response signature may computed as:
2ndorder response signature=linear residual−2ndorder residual
while a third-order response signature may be computed as:
3rdorder response signature=2ndorder residual−3rdorder residual

Such calculations may be used due to the correlations between control model parameters, especially between linear and 3rdorder parameters. The reason for order separation is that specific order is often related to a specific process change. For example, thermal process changes often generate 2ndorder response signatures. Therefore, such order separation monitoring may allow for specific process monitoring for process changes that result in high order response signatures in production wafers.

In order to simplify data for analysis, correctables in each group can be combined into a reduced set of parameters. For example, a set of correlated parameters may be transformed into a smaller set of uncorrelated parameters by orthogonal linear transformation where a single variable is utilized to account for as much of the variability in a subject data set as possible. Employing such a method may allow for the reduction of parameters to a more manageable level.

For example as shown inFIG. 4A, plots of 20 different process control model parameters employed over 7 production lots is shown. Alternately, as shown inFIG. 4B, those 20 process control parameters are grouped according to their order and the combined values are presented. For example, as shown in4B, those process control model parameters having a 2ndorder response signature are combined and represented by a portion of a bar graph element. Similarly, those process control parameters having a 3rdorder response signature are combined and represented by a second portion of the bar graph element. The total value represented by the bar graph element is the total of the 2ndorder and 3rdorder process control parameters. As can be seen fromFIG. 4B, with respect to Lot-4, process control parameters having a 2ndorder response signature account for a greater percentage of the total process control parameters. As such, the trend chart ofFIG. 4Bmay assist a user in determining a root-cause of a process response signature (e.g. process tools, recipes, and the like).

Still further, APC process model prediction performance may be enhanced through use of reference overlay feed-forward methodologies for a given lot. Referring toFIGS. 5A,5B and5C, various overlay conditions are shown for a first layer, second layer and a third layer.

Traditional method of overlay correction prediction may be defined by:
Overlay(predicted:next layer)=Overlay(input:current layer)−Overlay(measured:current layer)
where Overlay(input:current layer) is overlay computed according to model parameters for a given layer and Overlay(measured:current layer) is an overlay measured at a metrology tool.

However, this method does not account for any overlay errors found in a preceding reference layer. For example, as shown inFIGS. 5A,5B and5C, a reference layer (e.g. the 2nd layer) may be minus-shifted, aligned or plus-shifted, respectively, relative to a subject layer (e.g. the 3rdlayer). Traditional APC prediction may provide the same process correction recommendation for all three conditions as the overlay correction prediction is based only on the characteristics of the 3rd layer itself.

Instead, it may be advisable to incorporate the overlay result associated with the reference layer into the computation of the prediction calculation for the current layer:
Overlay(predicted:next layer)=Overlay(input:current layer)−Overlay(measured:current layer)−Overlay (measured:reference layer)*k
where k is a weighting factor.

Specifically, in computing a predicted process correction for a 4thlayer to be disposed over the 3rdlayer, the prediction calculation may be:
Overlay(predicted:4thlayer)=Overlay(input:3rdlayer)−Overlay(measured:3rdlayer)−Overlay (measured:2ndlayer)*k

As described above,