Fast and High-Precision Temperature Control in Semiconductor Vertical Furnace via Iterative Experiments

Abstract

The semiconductor vertical furnace is a key component in semiconductor manufacturing, used for heat treatment processes such as oxidation, layer deposition, and annealing. Improving the speed of temperature control in this equipment is critical for increasing productivity, particularly by shortening the time required for large temperature changes, known as thermal ramps. Although modeling a linear time-invariant (LTI) system is effective around a fixed operating temperature, it becomes inaccurate during rapid heating and cooling processes that involve large temperature changes, especially when faster control performance is required. As a result, conventional model-based control methods often fail to deliver both fast and accurate temperature regulation in practical scenarios. The aim of this paper is to develop a data-driven approach that enables high-speed, high-precision temperature control in a semiconductor vertical furnace. The proposed method is based on an iterative experimental procedure that refines the control model using actual measurement data. Experimental results show that the approach achieves the target temperature accurately within just four iterations. Compared to the conventional Linear-Quadratic-Gaussian (LQG) control method, it reduces the settling time to within ±1°C of the setpoint by 18% and lowers energy consumption by 20%. These findings demonstrate that the proposed data-driven method enables faster, more accurate, and more energy-efficient temperature control in semiconductor vertical furnaces.