Multi-Zone Heater Temperature Estimation for Substrate Processing

The patent uses a single temperature sensor to measure the temperature of one zone, then creates virtual copies of this measurement through mathematical modeling and machine learning algorithms to estimate temperatures in other zones. This avoids installing multiple physical sensors while achieving the effect of multi-zone temperature monitoring.

The patent replaces the mechanical approach of installing multiple physical temperature sensors with a computational approach using machine learning models, correlation formulas, and algorithms to estimate temperatures in multiple zones from a single sensor measurement, thereby reducing hardware complexity.

Instead of simple ratio control, the patent creates accurate virtual copies of temperature measurements in all zones through machine learning models that learn the complex relationships between heater zone temperatures, allowing precise temperature estimation without additional physical sensors.

The patent transforms the temperature control approach by using machine learning models that dynamically adjust temperature estimates based on multiple parameters including heater power ratios, thermal conductivity, specific heat, and learned correlations from training data, rather than simple fixed ratio control.

The patent performs preliminary temperature modeling and machine learning training during the setup phase to create accurate predictive models of heater zone temperatures. Once trained, these models enable rapid temperature estimation and adjustment without requiring extensive iterative tuning during actual semiconductor processing, thereby improving productivity.

The patent implements a feedback mechanism where the single temperature sensor provides real-time temperature measurements that are fed into the machine learning model, which then predicts temperatures in all zones and provides feedback for adjustments, enabling rapid convergence to uniform temperature distribution without extensive manual tuning.

The patent uses a single physical temperature sensor to measure one zone's temperature, then creates virtual measurements for all other zones through machine learning models that learn the thermal relationships between zones, effectively copying the measurement capability without additional physical sensors.

The patent makes a single temperature sensor serve multiple functions by using it to measure one zone's temperature while simultaneously providing data that enables estimation of temperatures in all other zones through the machine learning model, thereby achieving multi-zone monitoring with single-sensor hardware.