A multilevel approach to the control of a chemical–mechanical planarization process

Abstract

A multilevel hierarchical control system has been designed and is being applied to chemicalmechanical planarization ͑CMP͒ process control. The current implementation of the control system incorporates closed-loop run-to-run ͑R2R͒ control and open-loop real-time monitoring, and can accommodate inter-cell control. The R2R control element is enabled via a generic cell controller ͑GCC͒ implementation that provides flexible automated control of the process and equipment, multiple control algorithm branches and fuzzy logic decision capability among the branches, simulation capabilities, hardware and software independence, and extensive GUI support for control and data analysis. The R2R element utilizes a linear approximation multivariate control algorithm ͑branch͒ that supports individual exponential weighted moving average ͑EWMA͒ modeling of advices ͑outputs͒, weighting of inputs, granularity, and input bounding. The real-time element of the control system utilizes a partial least squares ͑PLS͒ algorithm to identify real-time equipment input trace patterns and relate these patterns to alarming conditions. The entire control system is designed to provide multivariate control of CMP process removal rate and uniformity. As a result of extensive design of experiments and testing, the R2R control level has been demonstrated to achieve good control of removal rate and fair control of uniformity. The addition of the real-time element is expected to improve process control and reduce R2R process noise, thus leading to a more effective R2R control element.

Neural network based uniformity profile control of linear chemical-mechanical planarization

“…Statistical surface response methods (SRM) are widely used to model the relationship between process parameters and responses in semiconductor manufacturing. Recently, SRM models have been developed to model the relationship between CMP removal rate and uniformity and process parameters [5]- [9]. Although the SRM models can give a fairly accurate CMP process model, they cannot predict precisely complex CMP processes in real-time under different environmental conditions.…”

Monitoring and control of semiconductor manufacturing processes

“…Static Regression Models Static models have found numerous applications in monitoring and control of semiconductor processes [21-[51, [36], [40], [41]. Although, in principle, the regression model can be polynomial of any degree, a linear model often performs satisfactorily and is widely accepted as a standard model for statistics-based monitoring and control.…”

“…The slow drift is primarily due to chemical residue buildup inside the processing chamber and/or the loading effect of various electrical equipment. Exponentially weighted moving average (EWMA) adaptation is typically employed to adjust the term d [40]- [43]. Further, occasional shifts due to material variations and scheduled maintenance operations can be captured by the variation of the model parameters in A (see [2] for further details).…”

“…Finally, the simplicity of the model allows for fast control computations. In fact, it has been successfully used in RtR control to generate suitable recipe adjustments for etching 121, 131, [5], photolithography [36], and CMP [40]- [42].…”

Run by run advanced process control of metal sputter deposition

“…The introduction of new sensors and process diagnostic tools, the maturation of existing technologies in these areas, and the development of integration tools and methodologies are making the application of APC methodologies a reality for the industry. Currently, there exists three primary focus areas for the application of APC techniques: statistical process control (SPC) with model-based process control (MBPC) [1]- [3], run by run (RbR) MBPC [4]- [10], and real-time process control (RTPC) [11]- [15]. The application of RTPC is often difficult due to current limitations of integration with tool software and reliability of, and longterm reproducibility of, real-time sensors [16].…”