Enhanced process and hardware architecture to detect and correct realtime product substrates
Embodiments disclosed herein include a processing tool for semiconductor processing. In an embodiment, the processing tool comprises a chamber, and a plurality of witness sensors integrated with the chamber. In an embodiment, the processing tool further comprises a drift detection module. In an embodiment, data from the plurality of witness sensors is provided to the drift detection module as input data. In an embodiment, the processing tool further comprises a dashboard for displaying output data from the drift detection module.
1. A processing tool, comprising:
a chamber;
a mass flow controller coupled to the chamber;
a mass flow meter coupled to the chamber;
a pressure gauge coupled to the chamber;
a plurality of witness sensors integrated with the chamber;
a drift detection module, wherein data from the plurality of witness sensors is provided to the drift detection module as input data from one or both of the mass flow controller or the mass flow meter; and
a dashboard for displaying output data from the drift detection module.
2. The processing tool of claim 1 , wherein the drift detection module utilizes machine learning algorithms to process the input data from the plurality of witness sensors.
3. The processing tool of claim 1 , wherein the drift detection monitor utilizes a hybrid model to process the input data from the plurality of witness sensors.
4. The processing tool of claim 3 , wherein the hybrid model comprises a physical model and a statistical model.
5. The processing tool of claim 1 , wherein the output data from the drift detection module comprises a statistical process control (SPC) chart.
6. The processing tool of claim 1 , further comprising:
a process correction module.
7. The processing tool of claim 6 , wherein the process correction module comprises:
a correction algorithm, wherein the output data from the drift detection module is fed into the correction algorithm as an input, and wherein an output from the correction algorithm is a control effort; and
a chamber control interface, wherein the control effort induces the chamber control interface to change one or more tool settings of the processing tool.
8. The processing tool of claim 7 , further comprising:
a process prediction module.
9. The processing tool of claim 8 , wherein the process prediction module comprises:
a continuous learning system;
a predictive algorithm; and
a self-correction module.
10. The processing tool of claim 1 , wherein the chamber is a lamp based chamber.
11. The processing tool of claim 10 , wherein the output data from the drift detection module includes one or more process parameters, wherein the one or more process parameters comprise one or more of a gas flow rate, a pressure, a temperature, a deposition characteristic, a coating amount on chamber walls, and a leak detection.
12. The processing tool of claim 1 , wherein the chamber is a heater based chamber.
13. The processing tool of claim 12 , wherein the output data from the drift detection module includes one or more process parameters, wherein the one or more process parameters comprise one or more of a pressure, a temperature, a deposition characteristic, a coating amount on chamber walls, and a radical density.
14. The processing tool of claim 1 , wherein the chamber is a plasma based chamber.
15. The processing tool of claim 14 , wherein the output from the drift detection module includes one or more process parameters, wherein the one or more process parameters comprise one or more of a gas flow rate, a pressure, a plasma density, a leak detection, a temperature, an RF parameter, and a coating amount on chamber walls.
16. A processing tool, comprising:
a physical tool, wherein the physical tool comprises:
a chamber;
a mass flow controller coupled to the chamber;
a mass flow meter coupled to the chamber;
a pressure gauge coupled to the chamber;
control loop sensors; and
witness sensors;
a drift detection module, wherein the drift detection module receives control loop sensor data and witness sensor data as inputs from one or both of the mass flow controller or the mass flow meter, and wherein the drift detection module outputs process parameter data that indicates if one or more processing parameters have drifted.
17. The processing tool of claim 16 , further comprising:
a process correction module, wherein the process correction module receives the process parameter data as inputs and outputs a control effort to change one or more of the tool settings of the physical tool.
18. The processing tool of claim 17 , further comprising:
a drift prediction module, wherein the drift prediction module receives the control loop sensor data and the witness sensor data as inputs, and wherein the drift prediction module outputs prediction data that indicates when the physical tool will operate outside of a threshold value.
19. A processing tool, comprising:
a physical tool, comprising:
a chamber;
a cartridge for flowing one or more processing gasses into the chamber from a plurality of gas sources;
a mass flow controller for each of the plurality of gas sources;
a mass flow meter between the gas sources and the cartridge;
a first pressure gauge between the mass flow meter and the cartridge;
a second pressure gauge fluidically coupled to the chamber; and
an exhaust line coupled to the chamber;
a drift detection module, wherein the drift detection module receives data from one or more of the mass flow controller, the mass flow meter, the first pressure gauge, and the second pressure gauge as inputs, and wherein the drift detection module outputs process parameter data.
20. The processing tool of claim 19 , wherein the drift detection module comprises one or both of a hybrid model comprising a physical model and a statistical model, and a machine learning module.