Advanced process control of a CVD tungsten reactor

Stefani, Jerry A.; Poarch, Scott; Saxena, Sharad; Mozumder, P.K.

doi:10.1109/66.536109

Cited by 20 publications

(8 citation statements)

References 18 publications

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“…The first strategy uses historical (or training) data and examines all possible F 2 values to obtain the best fixed propagation parameter, 2 F , with minimum mean square error: (10) where k′ is the index of the training data. Then, 2 F is used in the testing data. …”

Section: E the Two-dimensional Propagation Parameter Tunermentioning

confidence: 99%

Two-Dimensional Pheromone Propagation Controller

Lee¹,

Lee²

2013

IJET

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Abstract-Traditionally, the semiconductor fabrication conducts several measurements within a wafer and condenses the measurements into output quality characteristics to progress advanced process control (APC). Therefore, traditional APC employs performance indices along "time" axis and losses the space information among different measurements within a wafer. In this paper, we present a new perspective on process control with pheromones propagation mechanism, in which we treats the disturbances within a two-dimensional layout wafer as digital pheromones,. Our novel space-effect algorithm is called the two-dimensional pheromone propagation controller (2D-PPC). The simulation results show 2D-PPC, which involves space-effect, can improve the uniformity of the wafer over the conventional time-effect controllers.Index Terms-Two-dimensional pheromone propagation controller, process control, two-dimensional pheromone basket, two-dimensional digital pheromone infrastructure, swarm intelligence I. INTRODUCTIONIn semiconductor manufacturing, run-to-run control adjusts the recipe slightly based on in-line measurements to even out disturbances. Disregarding the methodology of the run-to-run control, some researches condense measurements to output quality characteristics [1]- [10]. Thus, traditional run-to-run controllers like EWMA employ the "time-effect", which means disturbance of a wafer affects a measurement or the output quality characteristics at different runs, among observed data to calculate the recipe for the next run and does not consider the "space-effect", which means disturbance of a wafer affects several measurements at the same time, among measurements within a wafer at a run.In this paper, we describe an algorithm that includes the effect among different measurements within a wafer. The concept comes from the observation that a disturbance of a wafer will affect a piece area, which may contain several measurements. The new algorithm uses swarm intelligence by assuming that the measurements have their own behavior and affect others nearby within a wafer at a run. Then, the intercepts with disturbances included of a linear regression model at different measurements within a wafer are modeled as digital pheromones. The interaction among the digital pheromones is modeled by a propagation mechanism. Because measurements within a wafer are a two-dimensional Manuscript received August 10, 2012; revised December 11, 2012. The authors would like to thank the National Science Council of the Republic of China for financially supporting this paper under Contract NSC 100-2221-E-009-063-MY2. D. S. Lee was with the Dept. of Mechanical Engineering, National Chiao Tung University, Taiwan (e-mail: dslee605@ ms37.hinet.net).A. C. Lee is with the Dept. of Mechanical Engineering, National Chiao Tung University, Taiwan (e-mail: aclee@ mail.nctu.edu.tw). layout, our novel algorithm is called the two-dimensional pheromone propagation controller (2D-PPC). This study modifies the propagation-out ratio of digital pheromone infrastruc...

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Section: E the Two-dimensional Propagation Parameter Tunermentioning

confidence: 99%

Two-Dimensional Pheromone Propagation Controller

Lee¹,

Lee²

2013

IJET

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“…[23][24][25][26] In this case in-line or off-line measurements are used to guide process recipe changes for succeeding wafer steps -either subsequent wafers in the same tool (feedback) or subsequent steps for the same wafer (feedforward). Such approaches can detect and compensate for longterm drift of process/equipment behavior, where longterm signifies variation evident over multiple wafers, but not detectable within a single wafer run.…”

Section: Advanced Process Controlmentioning

confidence: 99%

In-Situ Metrology: the Path to Real-Time Advanced Process Control

Rubloff

2003

AIP Conference Proceedings

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Abstract. While real-time and in-situ process sensors have been effectively applied to fault detection, process control through course correction has been mainly focused on in-line metrologies to drive run-to-run feedback and feedforward control We have developed in-situ metrologies based on mass spectrometry, acoustic sensing, and FTIR techniques which enable real-time thickness metrology and control in CVD processes at a level of about 1% accuracy. These developments open the door to real-time sensors as the basis for both fault management and course correction, i.e., for real-time advanced process control We have also employed in-situ metrology to develop robust control schemes for CVD precursor delivery from solid sources, and we are exploring a new spatially programmable reactor design paradigm for which real-time, in-situ sensing, metrology, and control of across-wafer uniformity is fundamentalAs indicated in Table 1, the goals of CC and FM are quite different: CC is aimed at keeping product quality on track, while FM is intended to monitor and maintain equipment so that product is not wasted and equipment repair is timely. The other characteristic which differentiates APC approaches is a distinction between run-to-run and real-time control: run-to-run

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“…This experimental approach to optimizing manufacturing processes by changing the process parameters has been very successful (e.g., Stefani et al, 1996). However, process engineers often focus on the process itself and may find it difficult to consider how process parameters changes affect the overall manufacturing system performance.…”

Section: Integrating Process Modelsmentioning

confidence: 99%

“…We included an RSM for W CVD in the network model to understand how process parameter changes affect lot makespan. For analyzing the W CVD cluster tool, we used an RSM for the W CVD process that was based on data collected by Stefani et al (1996). The deposition rate RSM has the following four process parameters: reactor pressure, deposition temperature, the mole fraction of WF 6 , and the mole fraction of H 2 .…”

Section: Integrating Process Modelsmentioning

confidence: 99%

Evaluating the impact of process changes on cluster tool performance

Herrmann

Chandrasekaran

Conaghan

et al. 2000

IEEE Trans. Semicond. Manufact.

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ISR develops, applies and teaches advanced methodologies of design and analysis to solve complex, hierarchical, heterogeneous and dynamic problems of engineering technology and systems for industry and government. ISR is a permanent institute of the University of Maryland ABSTRACTCluster tools are highly integrated machines that can perform a sequence of semiconductor manufacturing processes. Their integrated nature can complicate analysis when evaluating how process changes affect the overall tool performance. This paper presents two integrated models for understanding cluster tool behavior. The first model is a network model that evaluates the total lot processing time for a given sequence of activities. By including a manufacturing process model (in the form of a response surface model, or RSM), the model calculates the total lot processing time as a function of the process parameter values and other operation times. This model allows one to quantify the sensitivity of total lot processing time with respect to process parameters and times.In addition, we present an integrated simulation model that includes a process model. For a given scheduling rule that the cluster tool uses to sequence wafer movements, one can use the simulation to evaluate the impact of process changes including changes to product characteristics and changes to process parameter values. In addition, one can construct an integrated network model to quantify the sensitivity of total lot processing time with respect to process times and process parameters in a specific scenario.The examples presented here illustrate the types of insights that one can gain from using such methods. Namely, the total lot processing time is a function not simply of each operation's process time, but specifically of the chosen process parameter values. Modifying the process parameter values may have significant impacts on the manufacturing system performance, a consequence of importance which is not readily obvious to a process engineer when tuning a process (though in some cases, reducing process times may not change the total lot processing time much). Additionally, since the cluster tool's maximum throughput depends upon the process parameters, the tradeoffs between process performance and throughput should be considered when evaluating potential process changes and their manufacturing impact .2

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Advanced process control of a CVD tungsten reactor

Cited by 20 publications

References 18 publications

Two-Dimensional Pheromone Propagation Controller

Two-Dimensional Pheromone Propagation Controller

In-Situ Metrology: the Path to Real-Time Advanced Process Control

Evaluating the impact of process changes on cluster tool performance

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