Methods and systems for controlling a semiconductor fabrication process
View Patent ↗Software for controlling processes in a heterogeneous semiconductor manufacturing environment may include a wafer-centric database, a real-time scheduler using a neural network, and a graphical user interface displaying simulated operation of the system. These features may be employed alone or in combination to offer improved usability and computational efficiency for real time control and monitoring of a semiconductor manufacturing process. More generally, these techniques may be usefully employed in a variety of real time control systems, particularly systems requiring complex scheduling decisions or heterogeneous systems constructed of hardware from numerous independent vendors.
1. A method comprising:
controlling operation of a semiconductor manufacturing system with a state machine to schedule processing of one or more workpieces, the state machine including a plurality of states associated by a plurality of transitions, each one of the plurality of transitions having a weight assigned thereto, the semiconductor manufacturing system forming a vacuum environment for processing the one or more workpieces and including a mid-entry load lock positioned between at least two other load locks to the vacuum environment, the mid-entry load lock configured to at least one of add the workpiece to the vacuum environment and remove the workpiece from the vacuum environment;
receiving data from the semiconductor manufacturing system;
calculating the weight assigned to each one of a number of possible transitions from a current state of the plurality of states by applying the data as inputs to a neural network; and
selecting a transition from the current state of the plurality of states by evaluating the weight assigned to each one of the number of possible transitions from the current state.
2. The method of claim 1 , wherein the semiconductor manufacturing system includes a vacuum tunnel-based cart workpiece transport facility combined with a vacuum robot-robot handoff linear processing facility.
3. The method of claim 1 , wherein the semiconductor manufacturing system comprises modularly assembled modules.
4. The method of claim 3 , wherein at least one semiconductor manufacturing module can be added to the semiconductor manufacturing system without reconfiguring existing modules.
5. The method of claim 1 , wherein processing of one or more workpieces includes at least one of heating one or more workpieces and cooling one or more workpieces.
6. The method of claim 1 , wherein the received data includes technical information related to a process associated with the one or more workpieces.
7. The method of claim 1 , wherein the received data includes workpiece edge sensing data.
8. The method of claim 1 , wherein at least one of the states represents a state of an item of hardware within the semiconductor manufacturing system.
9. The method of claim 1 , wherein at least one of the states represents a position of a workpiece within the semiconductor manufacturing system.
10. The method of claim 1 , wherein at least one of the states represents a position of an isolation valve within the system.
11. The method of claim 1 , wherein the state machine is updated in substantially real time.
12. The method of claim 1 , wherein the state machine is updated every 20 milliseconds.
13. The method of claim 1 , wherein the inputs to the neural network include at least one process time for a workpiece within the semiconductor manufacturing system.
14. The method of claim 13 , wherein the at least one process time includes one or more of a target duration, a start time, an end time, and an estimated end time.
15. The method of claim 1 , wherein the inputs to the neural network include one or more of sensor data, temperature data, a detected workpiece position, an estimated workpiece temperature, an actual workpiece temperature, a valve state, an isolation valve state, robotic drive encoder data, robotic arm position data, end effector height data, a process time, a process status, a pick time, a place time, and a control signal.
16. The method of claim 1 , further including a second mid-entry load lock, wherein the mid-entry load locks are disposed in a vertical stack.
17. A computer program product comprising computer executable code embodied in a computer readable medium that, when executing on one or more computing devices, performs the steps of:
controlling operation of a semiconductor manufacturing system with a state machine to schedule processing of one or more workpieces, the state machine including a plurality of states associated by a plurality of transitions, each one of the plurality of transitions having a weight assigned thereto, the semiconductor manufacturing system forming a vacuum environment for processing the one or more workpieces and including a mid-entry load lock positioned between at least two other load locks to the vacuum environment, the mid-entry load lock configured to at least one of add the workpiece to the vacuum environment and remove the workpiece from the vacuum environment;
receiving data from the semiconductor manufacturing system;
calculating the weight assigned to each one of a number of possible transitions from a current state of the plurality of states by applying the data as inputs to a neural network; and
selecting a transition from the current state of the plurality of states by evaluating the weight assigned to each one of the number of possible transitions from the current state.
18. A system comprising:
a state machine that controls operation of a semiconductor manufacturing system to schedule processing of one or more workpieces, the semiconductor manufacturing system forming a vacuum environment for processing the one or more workpieces and including a mid-entry load lock positioned between at least two other load locks to the vacuum environment, the mid-entry load lock configured to at least one of add the workpiece to the vacuum environment and remove the workpiece from the vacuum environment, the state machine including a plurality of states associated by a plurality of transitions, each one of the plurality of transitions having a weight assigned thereto, wherein when the state machine is operating within one of the plurality of states, a selection of a transition from the one of the plurality of states to another one of the plurality of states is determined by evaluating the weight assigned to each one of a number of possible transitions from the one of the plurality of states; and
a neural network that receives as inputs data from the semiconductor manufacturing system and provides as outputs the weights for one or more of the plurality of transitions.
19. The system of claim 18 , wherein at least one of the states represents a state of an item of hardware within the semiconductor manufacturing system.
20. The system of claim 18 , wherein at least one of the states represents a position of a workpiece within the semiconductor manufacturing system.