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 system comprising:
a plurality of sensors for sensing aspects of a semiconductor manufacturing system;
a neural network enabled controller for controlling the operation of semiconductor manufacturing system components to schedule processing of one or more semiconductor workpieces, the neural network for receiving data from the plurality of sensors and effecting, through a finite state machine, a determination of changes in physical states of the semiconductor manufacturing system components; and
a graphical user interface that displays a real time three-dimensional model of changes in the physical state of the semiconductor manufacturing system components, wherein the real time three-dimensional model includes one or more objects that are correlated to an inventory containing data about a physical inventory of the semiconductor manufacturing system components, the real time three-dimensional model being responsive to data, from the finite state machine, indicating changes in the physical state of the semiconductor manufacturing system components, from the plurality of sensors and configured to show real time changes in conditions of the one or more objects during at least processing of the one or more semiconductor workpieces.
2. The system of claim 1 , wherein the semiconductor manufacturing system includes a tunnel-based cart workpiece transport facility combined with a robot-robot handoff linear processing facility.
3. The system of claim 1 , wherein the semiconductor manufacturing system comprises modularly assembled modules.
4. The system of claim 3 , wherein at least one semiconductor manufacturing module can be added to the semiconductor manufacturing system without reconfiguring existing modules.
5. The system of claim 1 , wherein processing of one or more workpieces includes at least one of heating and cooling.
6. The system of claim 1 , wherein the received data includes technical information related to a process associated with the workpiece.
7. The system of claim 1 , wherein the received data includes workpiece edge sensing data.
8. The system of claim 1 , wherein data from the semiconductor manufacturing system represents a physical state of an item of hardware within the semiconductor manufacturing system.
9. The system of claim 1 , wherein data from the semiconductor manufacturing system represents a position of a workpiece within the semiconductor manufacturing system.
10. The system of claim 1 , wherein data from the semiconductor manufacturing system represents a position of an isolation valve within the system.
11. The system of claim 1 , wherein the inputs to the neural network include at least one process time for a workpiece within the semiconductor manufacturing system.
12. The system of claim 11 , 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.
13. The system 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.
14. A method comprising:
controlling operation of semiconductor manufacturing system components with a neural network to schedule processing of one or more semiconductor workpieces where the neural network effects, through a finite state machine, a determination of changes in physical states of the semiconductor manufacturing system components;
receiving sensor data from the semiconductor manufacturing system components as inputs to the neural network;
displaying on a graphical user interface a real time three-dimensional model of changes in the physical state of the semiconductor manufacturing system components, wherein the real time three-dimensional model includes one or more objects that are correlated to an inventory containing data about a physical inventory of the semiconductor manufacturing system components, the real time three-dimensional model being responsive to data from the finite state machine indicating changes in the physical state of the semiconductor manufacturing system components, and configured to show real time changes in conditions of the one or more objects during at least processing of the one or more semiconductor workpieces.
15. The method of claim 14 , wherein data from the semiconductor manufacturing system represents a physical state of an item of hardware within the semiconductor manufacturing system.
16. The method of claim 14 , wherein data from the semiconductor manufacturing system represents a position of a workpiece within the semiconductor manufacturing system.
17. The method of claim 14 , wherein data from the semiconductor manufacturing system represents a position of an isolation valve within the system.
18. The system of claim 1 , wherein the one or more objects of the real time three-dimensional model of the semiconductor manufacturing system include hyperlinks configured to provide information related to a respective one of the one or more objects.