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Optimizing the Semiconductor Enterprise: Accounting for the Unplanned

March 17, 2009

From the time an order hits the books until it is shipped, how do we get the best performance possible out of a semiconductor enterprise? Sophisticated concepts and tools abound to answer this question. Most tools center on bringing orders, WIP, tool states, staffing, to name a few components, into a central tool or database. This data is then simulated forward though the factory processes and the outcome evaluated. Central to getting a realistic outcome, (predicted cycle time, lot out dates, etc.), is accounting for the unplanned events. The unexpected is what destroys cycle time and on-time delivery. It comes from all parts of the enterprise: customers, tools, staff, and so on. Predicting the unplanned events is a central feature of ManSim product offerings. Mathematical distributions are chosen that best represent the availability patterns of the tools in fab, test and assembly. During a simulation run, a random number generator uses the distribution to mimic tool behavior. The ManSim model forces lots to stop and wait for wafer processing, (at down equipment).1 The resulting variation builds WIP and increases cycle time. In this way ManSim products predict unplanned tool or staffing events. In a simulation run, orders are started, lots assigned to tools for processing, material is consumed, unplanned events are generated and the finished product allocated to its order. Statistical reports are generated predicting cycle time, lot out dates and tool utilization. By accounting for unplanned capacity losses or material shortages a realistic outcome is predicted. Problem-solving what-if analysis solutions can be tested with the model to optimize cycle time and on-time delivery. The best solution can found and implemented to improve the fab metrics.

  1. Using unexpected cycle time values to manage change” 9/30/2008 By Jim Schumacher and Dick Zuanich, of ManSim Inc.

Simulate before you invest

March 3, 2009


Challenges to running a profitable semiconductor fab in 2009 and beyond are well documented. ISMI has produced its 19 point guideline to enable next generation factory vision. Top items on this guideline for moving from 300mm prime to 450mm profitably are improved productivity and cost reduction.1 But, how to predict which of the many choices to achieve these goals, will be the most likely to succeed in producing greater fab productivity reduced costs and profits? Discrete event simulation is one way to answer these questions. In the 2005 paper “Getting ready for the simulation revolution in 300mm fab productivity!”2 John Schmitz, COO, SEMATECH 2005 stated “The new economy for microelectronics – with multibillion-dollar FABs and budget-busting technologies will force IC manufacturers to seek unprecedented levels of productivity over the next five years. John is right on target. We should raise the standard for seeking the best alternatives to improving our company’s bottom line. With $1.2 billion plus 300 mm FABs, discrete event simulation modeling, should become the standard and will certainly be one of the most cost effective alternatives for improving productivity and growth in the future”.2

    Whether taking on  the gigantic task of designing the first  450mm fab pilot line, or looking for productivity improvements and cost reductions in your existing fab, discrete simulation of your many options prior to investment is a great way to rank the alternatives by cost and productivity gains, before you spend a king’s ransom implementing them. By simulating proposed fab changes on a validated, verified model of your current or future factory you get to see the effect of those changes on the entire system. Often there will be unintended side effects from these proposed changes. These become visible in the graphics and reports.

   Using your key metrics as your yardstick you can quickly rank the simulation results of each change. “Simulation models more accurately predict the behavior of real world manufacturing systems than calculations based on static data derived from history. Subjecting these static data inputs to the effects of stochastic variability improves knowledge of the viable ranges of facility performance. Event simulation (simulation of the time domain) expands this knowledge further by creating and estimating the queues and delays that are generated by part flow variability and processing time variability for different products and their lot sizes, as well as the impact of resources (the capability and availability of personnel and machines).” 3  

   My mantra is simulate before you invest. Simulation is the best predictor of change outcome. Ranking and comparison of the alternatives will give you the most bang your dollar invested.



1 ISMI  NGF and 450mmindustry briefing    2008 SEMICON West  power

2 Getting ready for the simulation revolution in 300mm fab productivity!
Collins, D.W.   Williams, K.   Dye, B.  

ACADZ Inc., Tempe, AZ;

This paper appears in: Semiconductor Manufacturing, 2005. ISSM 2005, IEEE International Symposium

3 Overview of ManSim products, ManSim Inc.