Data processing: generic control systems or specific application – Specific application – apparatus or process – Product assembly or manufacturing
Reexamination Certificate
2001-08-22
2004-03-02
Picard, Leo (Department: 2125)
Data processing: generic control systems or specific application
Specific application, apparatus or process
Product assembly or manufacturing
C700S036000, C700S097000, C700S106000, C705S002000, C705S029000
Reexamination Certificate
active
06701201
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention generally relates to a computer implemented decision support system for determining a production schedule of feasible material releases within a complex multi-stage manufacturing system architecture.
2. Background Description
A key requirement for successful operations management in any manufacturing industry is the coordination of both available and future supply with both existing and future demand. This process is referred to as supply-chain management. Large-scale manufacturing systems, such as those encountered in semiconductor manufacturing, involve complex distributed supply/demand networks. These large-scale manufacturing systems are linked with suppliers and distribution channels worldwide and have global manufacturing networks comprising, for example, a single site to dozens of sites. Analyzing and coordinating internal manufacturing logistics with external links to suppliers and distribution channels is critical to optimizing material flows, managing product mix profiles, and evaluating manufacturing system design changes.
Semiconductor manufacturing is a complex and refined process involving everything from growing silicon crystals, the source of silicon wafers upon which integrated circuits are grown, to the actual placement and soldering of chips to a printed circuit board. Initially, raw wafers are cut from a silicon ingot and processed through a sequence of work centers. The end goal of this process is to build a set of integrated circuits on the surface of the silicon wafer according to a specific circuit design. This process involves repeatedly applying four basic steps: (i) deposition, (ii) photolithography, (iii) etching and (iv) ion implantation. These steps are the processes by which materials with specific dielectric properties (e.g., conductors, insulators) are deposited on the surface of the wafer according to the precise circuit design specifications. These processes are repeated many times to build up several layers (typically between 12 and 25) of the circuits.
Once the circuits have been built on the wafers they are tested to determine the resultant yield of operational circuits and tagged for reference. Circuits are then diced and sorted, and subsequently wire bonded to a substrate to assemble a module. These modules, which are further tested to determine electromagnetic and thermal characteristics, are eventually combined on printed circuit boards to make cards. Finally, the cards are tested and those that pass are eventually used in the assembly of a wide range of finished electronic products (e.g., PCs, printers, CD players, etc.). From the point of view of semiconductor manufacturing, the modules and cards are the finished products taken to market.
There are many aspects of extended enterprise supply chain planning (EESCP) systems which affect the generation of production plans. For example, a large number of different parts are required to produce finished products such as modules and cards. From the above example, producing a module requires several subcomponent items including, but not limited to, a silicon wafer, a wire lead frame, substrate and the like. Furthermore, production of a module requires that several different types of resources be available (e.g., work centers, personnel and the like) for operations such as dice, pick and sort, testing, etc. For a particular manufacturing plant it is often (but not always) the case that there is only one way to produce a particular part number (PN). However, at the EESCP level it is common for there to be multiple sources from which to obtain necessary material supply as well as multiple options for resources to use for production. These options for supply and capacity resources to meet demand create complex tradeoffs involving decisions such as:
which plant to source a particular part number (PN) from,
which process in a given plant to use to make a PN,
material substitutions, and
whether to build inventory, and so on.
At a macro level, the problem involves optimally balancing material flows across a supply/demand network given finite available capacity, geographically differentiated supply and demand locations, material processing costs, inventory holding costs, parametric data (e.g., product yields, cycle times, etc.) and the like.
The complicated process architecture in the semiconductor manufacturing industry creates unavoidably long lead times for processing through all manufacturing stages to produce finished products. These production lead times necessitate the advance planning of production so that material releases throughout the production system are coordinated with the end customers' demand for any of a wide range of finished products (typically on the order of thousands in semiconductor manufacturing). Such advance planning depends on the availability of finite resources (finished goods inventory, work in process (WIP), workcenter capacity, etc.) and may tradeoff utilization of the resources at different locations, using different processes.
Planning and scheduling functions within the semiconductor manufacturing industry can be categorized in various ways. The following summary (Sullivan and Fordyce, “IBM Burlington Logistics Management Systems”, Interfaces, 20, 1, 43-64, 1990), is based on a tier system in which each tier is defined by the time frame to which the decisions pertain.
Tier 1: Long range (3 months to 7 yr) strategic level decisions such as mergers, capacity acquisition, major process changes, new product development, and long term policy based decisions.
Tier 2: Medium range (1 week to 6 months) tactical scheduling involving yield and cycle time estimation, forecasting and demand management, material release planning and maintenance scheduling.
Tier 3: Short to medium range (weekly planning) operational scheduling for optimizing consumption and allocation of resources and output of product, demand prioritization techniques, capacity reservation and inventory replenishment.
Tier 4: Short range (daily) dispatch scheduling for addressing issues such as machine setups, lot expiration, prioritizing of late lots, job sequencing, absorbing unplanned maintenance requirements and assigning personnel to machines.
Due to the complicated process architecture and unavoidably long lead times to complete processing through all manufacturing stages for a finished product, advance planning decisions are a necessity. The above taxonomy of planning and scheduling decisions is hierarchical; that is, decisions in higher tiers affect decisions in the lower tiers. For example, long range capacity acquisition decisions determine eventual yield and cycle times, the available resources that can be utilized, and the extent to which maintenance needs to be scheduled in the future. Decisions in higher tiers, by the nature of their long time frames, are made under considerable uncertainty, and seek to anticipate future requirements based on current information. On the other hand, lower level tier decisions are of a corrective/reactive nature and act to absorb uncertainty not accounted for in the higher tiers. As an illustration, advanced production planning and scheduling decision support systems are typically run on a weekly basis, however, the planning horizon for such runs may range several years depending on the planning horizon of interest and the level of detail in forecasting. Thus, advance planning systems may impact decisions in tiers 1, 2 and 3 which, in turn, affect tier 4 decisions. Therefore, the matching of assets to demand at the EESCP level is a major planning activity which affects decisions within all tiers.
If unlimited assets were available, then the matching of demand with assets would be straightforward. In reality, however, finite supply and capacity create constraints on production scheduling. These constraints make the determination of a feasible production schedule (let alone an optimal one) a complex problem.
Major Production Planning Activities
The large-scale nature of production plann
Hegde Sanjay R.
Milne Robert J.
Orzell Robert A.
Pati Mahesh C.
Patil Shivakumar P.
Kotulak Richard
McGuireWoods LLP
Picard Leo
Rodriguez Paul
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