1. Field of the Invention
The present invention relates generally to a process for the modeling and control of production integrated-processing equipment (PIPE). It further relates to a class of PIPEs which have operations sequences. It also relates to the use of PIPE-specific models to control material movement within the PIPE and loadings of the PIPE. It also relates to the use of such PIPE-specific models to analyze and extrapolate PIPE performance. It also relates to the use of PIPE-specific models to evaluate and develop PIPE designs and PIPE control schemes. The invention further relates to a computer integrated manufacturing (CIM) system that includes a PIPE-specific model for automatic PIPE control and to the use of PIPE-specific models in the design of CIM systems.
2. Description of the Prior Art:
Many manufacturing plants or factories are using or introducing highly-complex production integrated-processing equipment (PIPE). This integrated equipment is also known as modular equipment or cluster tools. PIPEs are capable of performing multiple, independent processing steps without leaving a controlled environment. By performing sequences of processing steps in one machine, substantial yield improvements over conventional processing equipment may result. This automated processing reduces contamination and unnecessary human handling. The complexity of this integrated equipment, however, introduces severe problems in analysis of the PIPE performance.
Prior to this invention, PIPE manufacturers and users have taken one of two approaches to understand PIPE performance: spreadsheets or physical experimentation. Spreadsheets provide a basis for approximate analytic calculations. While such an approach results in computer calculations that provide impressive-looking numeric results, little or no effort is expended in establishing the underlying accuracy of such spreadsheet calculations. A major area of approximation is the description of internal movement through the PIPE. This internal material movement is complex, and poorly understood. One approximation in spreadsheet calculations has been to surrender to this complexity and describe the operations sequence or path through the PIPE as being random.
The second approach taken by PIPE manufacturers and users has been physical experimentation. There is clearly a need for performance analysis, especially with regard to throughput and cycle time, since PIPEs, in general, are multi-million dollar pieces of equipment. A single physical experiment can cost tens-of-thousands of dollars and provide only one data point. The need for performance analysis has compelled manufacturers and users to perform such experiments in order to improve on the accuracy of spreadsheets. Millions of dollars may be spent doing performance analysis by this method. Any answers obtained by this method are case-specific, and any change in physical design, operating conditions, or recipes will negate the results and require repeating the effort.
Performance Analysis and PIPE Productivity
One of the goals of performance analysis is to calculate the cycle time of a standard manufacturing unit, i.e., part, lot, or cassette, as a function of process recipe. The process recipe is the set of processing steps to be performed by the PIPE. Despite the processing advantages provided by PIPEs, realistic calculations of throughput and capital cost per part impact the user's choice of a PIPE for a particular process step or steps. The calculation of throughput of a PIPE workstation is based primarily on the PIPE's cycle time.
Table 1 demonstrates the impact of cycle time on the capital cost per part. In this example, 25-wafer cassettes are being processed by a PIPE. The PIPE in this example has a modest capital cost of $1 mm which is amortized over 5 years. The capital cost per part is a direct function of the cycle time of the cassette in the PIPE. A variation of a factor of four in cycle time results in a variation of a factor of four in the capital cost per part. Clearly the accurate calculation of PIPE cycle time is crucial information in making decisions on equipment selection and numbers of machines needed at a workstation. Further, the total operating cost per wafer may be twice the capital cost.
TABLE 1 ______________________________________ PIPE PRODUCTIVITY vs CYCLE TIME ASSUMPTIONS: PIPE CAPITAL COST = $1,000,000 5 YEAR AMORTIZATION ($200,000/yr) 325 WORK DAYS ($615 in amortization/d) 20 PROCESSING HOURS PER DAY CASE 1 CASE 2 ______________________________________ CYCLE TIME 0.5 HRS 2 HRS # CASSETTES/d 40 10 COST/CASSETTES $15.38 $61.50 COST/Part $0.62 $2.45 ______________________________________