Applied Materials, Inc.

David Everton Norman

Richard Stafford

Paul D Hoad

Fumio Kawada

Madhav R Kidambi

Tae Young Ahn

Alan George Howells

Keith Pare

Andrew Charles Carson

Steve Marteney

Gordon Lance Jessup

Embodiments presented herein provide techniques for planning and scheduling a semiconductor back end factory. The technique begins by running a first mathematical programing model applied to factory data and production targets that produces a solution and processing the solution to produce a bottleneck loading plan for at least one machine of the factory's bottleneck machine families. The technique further includes running a second mathematical programing model applied to the bottleneck loading plan that produces a solution and processing the solution to produce a conversion schedule for the at least one machine of the bottleneck machine families. The technique further includes creating a lot schedule for the factory by running a simulation that follows the conversion schedule. The technique further includes publishing the lot schedule.

Method for improving semiconductor back-end factories

Embodiments presented herein provide techniques for executing a block-based (BB) workflow to solve a constraint programming (CP) model related to a semiconductor manufacturing environment. Embodiments include receiving at least one BB workflow comprising a plurality of blocks. The plurality of blocks may specify a set of operations. Embodiments include accessing a plurality of block definitions corresponding to the plurality of blocks. Embodiments include executing the at least one BB workflow by performing the set of operations based on the plurality of block definitions, including extracting data from the semiconductor manufacturing environment, the data comprising both static data and dynamic data related to equipment in the manufacturing environment, creating the CP model based on the data and at least one constraint defined in the BB workflow, using a solver to determine a solution to the CP model; and publishing the solution to at least one component in the semiconductor manufacturing environment.

Constraint programming using block-based workflows

A method and system for scheduling tasks using a counter constraint. A method may include identifying multiple tasks to be performed, receiving dependency data indicating that scheduling of at least one task is dependent on whether a counter satisfies a threshold in relation to an additional condition, and upon determining, during scheduling, that the counter satisfies the threshold in relation to the additional condition, triggering a scheduling action with respect to at least one task.

Counter and timer constraints

Embodiments presented herein provide techniques for planning and scheduling in a factory. The technique begins by generating a bottleneck loading plan from a plurality of inputs. A simulation is run using the bottleneck loading plan. The factory is simulated using decisions made based on the bottleneck loading plan and a lot-to-machine schedule is generated with the simulation bottleneck loading plan.

Method for back end planning and scheduling

Embodiments presented herein provide techniques for generating and optimizing a plan in a manufacturing environment. The techniques begins by receiving a plurality of demands for a plan, wherein each demand of the plurality of demands has parameters specifying a set of operations, a due date, user specified business logic and priority. The demands are ranked based on the parameters and the user specified business logic. The plurality of demands is broken into sets of demands based on the a predefined number and the demand rank. The demands in a first set of demands are optimized to generate a strategy for fulfilling the demands in the first set of demands. One or more constraints are applied to the first set of demands to ensure the first set of demands is fulfilled in preference to the remaining sets of demands.

Method for fulfilling demands in a plan

Embodiments presented herein provide techniques for executing a block-based workflow to perform a planning process for a semiconductor manufacturing environment. The block-based workflow includes a plurality of blocks that specify a set of operations for performing the planning process. One embodiment includes extracting planning data from a spreadsheet application, converting the data from a first schema to a second schema, generating a plan for the semiconductor manufacturing environment, and publishing the plan to the spreadsheet application, based on the plurality of blocks in the block-based workflow.

Planning for manufacturing environments

Embodiments presented herein provide techniques for executing a block-based workflow to provide a schedule for a semiconductor manufacturing environment. The block-based workflow includes a plurality of blocks and each block specifies a set of operations to be performed upon execution of each block. One embodiment includes extracting scheduling data from the semiconductor manufacturing environment, determining an allocation of the number of lots to one or more devices operating in the semiconductor manufacturing environment, determining an order in which the lots should be processed by the one or more devices and publishing results of the allocation and processing order to at least one another device in the semiconductor manufacturing environment, based on the plurality of blocks in the block-based workflow.

Scheduling in manufacturing environments

A method for sequencing a plurality of tasks performed by a processing system and a processing system for implementing the same are disclosed herein. In one embodiment, a method for sequencing a plurality of tasks performed by a processing system is provided that includes generating a schedule by iteratively performing a scheduling process and processing a plurality of substrates using the plurality of semiconductor processing equipment stations according to the schedule. The scheduling process uses highly constrained tasks and determines whether a portion of the first list of the highly constrained tasks exceeds a capacity of the processing system. The scheduling process further includes updating the latest start time and the earliest start time associated with each of the plurality of tasks yet to be scheduled based on the assigned task.

Method for sequencing a plurality of tasks performed by a processing system and a processing system for implementing the same

A computing system receives user input of scheduling problem data. The scheduling problem data relates to a scheduling problem and includes one or more stations and tasks to be performed by at least one station. The computing system constructs a graph problem using the scheduling problem data. The graph problem includes a graph. The computing system cuts the graph into sub-graphs using a cut algorithm to create a cut result that satisfies a threshold and identifies one or more task exceptions from the sub-graphs in the cut result. The one or more task exceptions are tasks that can be assigned to more than one sub-graph. The computing system creates scheduling sub-problems pertaining to the one or more task exceptions using the cut result.

Method and system for splitting scheduling problems into sub-problems

Methods and systems are provided for filtering lot schedules of a manufacturing facility. A first schedule is identified, in which the first schedule is a previously executed schedule. A processing device generates a pool of lots to be scheduled using information from the first schedule, in which the pool of lots is a subset of a plurality of lots associated with the first schedule. The processing device generates a second schedule using the pool of lots.

Method and system for filtering lot schedules using a previous schedule

A computer system identifies a dispatching rule that corresponds to a dispatching decision to be made in a simulation of a production environment. The dispatching rule is associated with a set of data processes. The computer system identifies a subset of the data processes that is to be executed in the simulation and executes the subset of data processes to simulate the dispatching rule to make the dispatching decision. The computing system generates a simulation decision that describes the dispatching decision made from executing the subset of data processes.

Configuring a dispatching rule for execution in a simulation

A method and system for the use of prediction data in monitoring actual production targets is described herein. In one embodiment, a process is provided to receive data from a plurality of source systems in a manufacturing facility and generate a prediction pertaining to a future state of the manufacturing facility based on the data received from the plurality of source systems. A recent state of the manufacturing facility is determined based on the data received from the plurality of source systems and a comparison between the recent state and the prediction is facilitated.

Use of prediction data in monitoring actual production targets

A per-station start time scheduler identifies a station that is unavailable to perform a task over a time interval. The scheduler identifies a plurality of stations and a start time of a task. The scheduler monitors each of the plurality of stations using a station time constraint corresponding to the task. Upon detecting that a station is unavailable to perform a task over a time interval, the scheduler sends a notification specifying the station that is unavailable to perform the task.

Per-station start time constraint

A server creates a plurality of projects pertaining to providing manufacturing service results. Each of the plurality of projects specifies operations to be performed relating to providing the manufacturing service results and at least one of the plurality of projects generates a requested manufacturing service result. The server initiates the at least one project that generates the requested manufacturing service result, wherein the at least one project is associated with an execution engine. The initiation of the at least one project may include sending input data to the execution engine based on configuration data that is associated with the execution engine, and obtaining the requested manufacturing service result from the execution engine. The server provides the requested manufacturing server result to at least one recipient.

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Bountiful, UT, United States of America

David Everton Norman