Application of feedforward and adaptive feedback control to semiconductor device manufacturing

Stoddard, K.; Crouch, P. E.; Kozicki, Michael; Tsakalis, K.

doi:10.1109/acc.1994.751872

Cited by 6 publications

(8 citation statements)

References 8 publications

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“…Control of semiconductor processes over multiple steps has been identified as an important feature of the semiconductor manufacturing facilities of the future [Lar94] and research has been conducted for developing control algorithms for carrying out such control [Lea96], [Sto94]. However, the development of enabling mechanisms which can be used to implement multi-step feedback and feed-forward control has not been adequately addressed (see Section 4 for further discussion).…”

Section: Motivation and Problem Descriptionmentioning

confidence: 99%

“…Another research effort in multi-step control is reported in [Sto94]. Feed-forward and feedback techniques are applied to a processing sequence involving the four basic steps of silicon oxidation, aluminum metallization, lithography and aluminum etch.…”

Section: Research In Multi-step Controlmentioning

confidence: 99%

“…By achieving the stated goal of keeping the capacitance of the fabricated capacitor fairly constant (within 1%), the study demonstrated the potential of using multi-step control. The control strategies described in [Sto94], as well as the strategies described in [Lea96] can be implemented by using the Active Controller. In fact, the control rules shown in the examples in Section 3 implement some of the control strategies described in these papers.…”

Section: Research In Multi-step Controlmentioning

confidence: 99%

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Active Controller: utilizing active databases for implementing multistep control of semiconductor manufacturing

Chaudhry

Moyne²,

Rundensteiner

1998

IEEE Trans. Comp., Packag., Manufact. Technol. C

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Whenever the fabrication process consists of several steps and the dynamic modification of individual steps or step sequences of the process is allowed, multi-step feedback and feed-forward control can be utilized to improve the quality of the fabrication process. Feedback and feed-forward control have been identified as important features of the semiconductor manufacturing facility of the future, and research has been carried out on developing suitable control algorithms. However, the issue of providing generic enabling mechanisms for multi-step control has not been adequately addressed. To fill this gap, we have developed the Active Controller -a generic, adaptable and reusable software enabler for multi-step control in manufacturing facilities. The Active Controller utilizes a recently emerging technology, called active databases, to define and automatically execute powerful and expressive rules for implementing multi-step control algorithms. The conditions of Active Controller rules are defined to check for scenarios in which multi-step control is needed. The Active Controller keeps track of relevant processing events and data, and when the conditions for multi-step control hold, executes appropriate actions to compensate for the errors in processing. We show that the Active Controller implementation, with its capability for the definition of complex rules over a history of processing events and ability to invoke user-provided analysis routines, provides for a generic and adaptable implementation that can be used to implement various algorithms for multi-step control. We also address the important issue of integrating the Active Controller with existing CIM environments and describe the Active Controller's interaction with CIM applications to highlight the fact that the Active Controller is not tied to a specific CIM implementation.

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Section: Motivation and Problem Descriptionmentioning

confidence: 99%

Section: Research In Multi-step Controlmentioning

confidence: 99%

Section: Research In Multi-step Controlmentioning

confidence: 99%

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Active Controller: utilizing active databases for implementing multistep control of semiconductor manufacturing

Chaudhry

Moyne²,

Rundensteiner

1998

IEEE Trans. Comp., Packag., Manufact. Technol. C

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“…Machines Ma and Mb in the Diffusion bay used predictive machine controllers (a PID controller compared to an H-infinity controller) 11,20 to establish when the machines were to be taken down for emergency maintenance. Predictive controllers are being researched for yield improvement to determine a potential failure prior to its occurrence so the product being processed is not scraped 7,11,13,17,18,19,20 . Machines Mc and Md in the Implant bay used historical data of Mean-Time-Before-Failure (MTBF) and Mean-Time-To-Repair (MTTR) as the emergency maintenance.…”

Section: The System Modelmentioning

confidence: 99%

Minimum Inventory Variability Dispatching Policies (Mivp)

Collins¹,

Flores-Godoy²,

Hoppensteadt³

et al.

2000 Annual Conference Proceedings

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This paper illustrates the use of discrete event stochastic simulation modeling to compare two scheduling (dispatching) policies for machines in a factory when a machine becomes available for processing. The two policies are first-in-first-out (FIFO) and Minimum Inventory Variability Policies  (MIVP ), both control the items in the queue (buffers) in front of the machine or resource 8,9,10,11. The simulation model is run with FIFO for each queue for 100 days to establish a baseline set of data. This baseline cycle time and work-in-progress (WIP) data are collected for comparison to MIVP . The only change between the model runs is that the queues in the model are switched to run the rule set of MIVP . With discrete event simulation modeling, the user can play "what if" scenarios without expended a lot of capital 4,5,8,9,10,11,19,20. The results from simulation give the user an additional input in making decisions. Examples of such a simulator use include the analysis of machine utilization, queue statistics, mean cycle time and mean WIP and production throughput, etc. This analysis can serve to push the bottleneck capacity to its limit, setup and test scheduling rules and preventive maintenance schedules, and determine personnel (operator) availability requirements. Thus, a good simulator allows for the investigation of complex "what-if" scenarios at a minimal cost, high speed, and without disturbing the normal production.

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“…The determination of an optimal scheduling policy remains highly nontrivial, involving the solution of a constrained optimization problem with respect to often-conflicting objectives while any admissible policy must possess certain robustness properties in the presence of uncertainty. This research has developed hierarchical simulation modeling software tools that can be used to manage variabilities arising on the FAB floor 3,4,5,6,7,8,10,11,15,18,23,24,25,26,31,38,43 . These tools will enable managers and operators to avoid work in progress (WIP) build-ups caused by these variabilities.…”

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confidence: 99%

Interdisciplinary Research On Modeling And Scheduling Of Semiconductor Manufacturing Operations

Collins

Golshani

Hoppensteadt

et al.

2000 Annual Conference Proceedings

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This paper will describe the ongoing interdisciplinary research work of a group of faculty and students working within the Systems Science and Engineering Research Center at Arizona State University (SSERC) in collaboration with INTEL and Motorola.One of the strongest driving forces in the economy of most developed countries is manufacturing. In the United States, one of the most important components of this driving force is the manufacturing of semiconductors in fabrication facilities (FAB). Not surprisingly, tremendous efforts have been expended to reduce the art of semiconductor manufacturing to a science.While the improvements in scale and yield of semiconductor manufacturing has been spectacular and are well known, the improvement in operational methods of process flow and process scheduling have not been as impressive. The most obvious reason for this is the inherent physical/chemical complexity of these processes. Semiconductor manufacturing processes are hundreds of steps long, and factories typically operate multiple processes with each one running many product devices to serve a dynamic marketplace. Process flows are re-entrant and the work in process can be subject to rework. Finite equipment and human resources exhibit a wide range of variability of availability and performance. There are batching or set-up considerations on machines that process by wafer, by lot, or by batch.One practical consequence of all of this complexity is that decisions must be made on very different time-scales. The smallest time-scale being every few minutes during floor operations, usually about resource allocation -which step/lot combination to run next, which down machine to repair next, and so on. The medium time-scale of every few days or weeks, responding to market changes. To the largest time-scale of every few months concerning process changes, design improvements, etc. The integration of all of these operational decisions defines the performance of the factory. The key difficulty in making these decisions lies in the intricacy by which they affect the long-and short-term factory performance, as well as each other.

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Application of feedforward and adaptive feedback control to semiconductor device manufacturing

Cited by 6 publications

References 8 publications

Active Controller: utilizing active databases for implementing multistep control of semiconductor manufacturing

Active Controller: utilizing active databases for implementing multistep control of semiconductor manufacturing

Minimum Inventory Variability Dispatching Policies (Mivp)

Interdisciplinary Research On Modeling And Scheduling Of Semiconductor Manufacturing Operations

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