Key technologies and optimization strategies for improving the temperature control accuracy of CVD p

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Key technologies and optimization strategies for improving the temperature control accuracy of CVD p

In the CVD (chemical vapor deposition) process, temperature is the core parameter that affects the deposition rate, crystal structure and performance of thin films, and its control accuracy directly determines the quality of the final product. Combining the existing technical solutions and control logic, improving the temperature control accuracy requires multi-dimensional parameter coordinated regulation and intelligent algorithm optimization.

1. Multi-parameter coordinated temperature closed-loop control system

The temperature distribution in the CVD reactor is affected by multiple factors such as substrates, heating elements and heat transfer media, and it is necessary to achieve precise control through multi-parameter linkage:

Dual feedback regulation of substrate top and bottom temperatures

By obtaining the current top temperature of each substrate, the preset top average temperature and the average temperature of the bottom of the substrate carrier, combined with the number of substrates, the bottom heating temperature is dynamically calculated to ensure the overall heating uniformity of the carrier. For example, when the top temperature of a substrate deviates from the preset value, local compensation can be achieved by adjusting the bottom heating power of the corresponding area.

Dynamic adaptation of heat transfer gas component coefficient
According to the deviation between the current top temperature of each substrate and the preset value, the component coefficient of the heat transfer gas (such as the ratio of inert gas to reactive gas) is adjusted in real time to optimize the heat conduction efficiency and reduce temperature fluctuations. For example, in the early stage of deposition, the thermal conductivity of the heat transfer gas can be increased to accelerate the temperature rise, and the coefficient can be reduced in the stable stage to suppress overshoot.

2. Optimization and upgrading of PID control algorithm
The traditional PID controller is susceptible to interference from nonlinear systems, and the adaptability needs to be improved through algorithm improvement:

Segmented PID parameter configuration
The variable speed integral PID algorithm is used to apply different proportional (P), integral (I), and differential (D) parameters in the temperature rise stage, overshoot stage, and stable stage. For example, the P value is increased in the temperature rise stage to shorten the response time, and the I value is reduced in the stable stage to avoid overshoot.

Sensor linearization and environmental compensation
The nonlinear characteristics of the temperature sensor are linearized by electronic circuits or programs to expand the effective measurement range; at the same time, the influence of external factors on the sensor accuracy is reduced through compensation of parameters such as ambient temperature and air pressure. For example, in a high temperature environment, the measurement error can be corrected by the thermocouple cold end compensation algorithm.

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3. Strengthening of hardware system and calibration mechanism

Selection of high-precision sensing and heating elements
Select high-performance temperature sensors (such as platinum resistance PT1000) and partitioned independent heating modules to ensure that the temperature signal acquisition resolution reaches ±0.1℃ and the heating power adjustment accuracy is 0.1%.

Periodic calibration and system diagnosis
Regularly calibrate sensors, heating elements and control modules to establish a temperature deviation database. For example, calibrate sensor drift through a blackbody furnace, use an infrared thermal imager to detect the temperature uniformity of the heating area, and replace aging components in time to maintain system stability.

4. Special optimization strategies in application scenarios

Multi-substrate uniformity control
In mass production scenarios, optimize the layout of heating elements (such as annular partition heating) through substrate stage temperature gradient simulation and heat transfer model simulation to ensure that the temperature difference between each substrate is ≤±1℃.

Adaptation to extreme process conditions
For high temperature (>1000℃) or low pressure CVD process, high temperature resistant ceramic heating tube and vacuum sealing design are adopted, combined with PID-fuzzy control hybrid algorithm, to balance fast response and control accuracy, and avoid film defects caused by temperature overshoot.

Through the coordinated application of the above technical means, the temperature control accuracy of CVD process can be improved to within ±0.5℃, significantly improving the uniformity of film thickness and crystal quality consistency, providing support for large-scale production in high-end fields such as semiconductors and optoelectronic materials.

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