ABSTRACT
The etching of SiO2 layers from silicon surfaces is one of the most critical steps in wet processing technology. Although numerous studies have been performed to analyze the mechanisms and kinetics of these processes, little attention has been given to monitoring and controlling the chemical concentrations in the process baths. Chemical concentration control is becoming crucial to wafer processing in order to obtain consistency and more cost-effective IC manufacturing. This paper demonstrates the use of conductivity sensors to monitor and control the concentration of HF etching solutions. Effects of etch byproducts on the conductivity measurements have been investigated. Once the etch byproducts are characterized and accounted for, results showed that a much more stable etch process can be obtained and the bath life can be extended even in the presence of etch by products.
INTRODUCTION
Life of etching baths can be extended for longer periods of time if an accurate and continuous control of chemical concentrations is provided [1]. Compared to standard analytical techniques for chemical concentration e.g. NIR, UV spectroscopy [1], conductivity cells provide fast, very cost-effective, and real-time control of HF concentration. If the temperature is held constant, and the conductivity of the HF solution is maintained, the etch rate of SiO2 can be accurately controlled as well [1].
Electrodeless conductivity sensors were used to accurately monitor and control the concentration of HF acid during the etching of thermal oxide from silicon surfaces [1]. However, the etch by-products can affect the linear conductivity-concentration relationship. This effect is magnified when etching thick oxide layers in dilute HF baths. A correction must be developed to correlate the amount of SiO2 etched with the change in conductivity [2]. Results showed that these techniques are suitable for monitoring and controlling the etch rate in IC manufacturing environment.
EXPERIMENTAL
The experiment was performed on Akrion’s Fully Automated GAMA wafer processing station in the Class 1 Application Laboratory at Akrion. Fifty 200-mm wafers with sufficient amounts of thermal oxide on both sides were prepared for the test. A standard HF process tank was used. The etching process was conducted at 21°C in an initially mixed 100:1 (H2O:HF) HF bath. The conductivity sensor for monitoring the HF bath was calibrated and the reading for the aforementioned HF concentration typically ranged from 6600 to 6800 µS/cm at 21°C. In addition, the bath’s conductivity and temperature were recorded by a PC with automated data acquisition software.
For the test of etching process characterization, a lot of 50 oxide wafers were immersed in the HF bath for 3 hours, without deionized water or HF injection, followed by rinse and dry. The characteristic curve of conductivity versus etch time under the specific system setting was developed and used as a key parameter for the control scheme.
For the test of new algorithm evaluation, a batch of 50 oxide wafers was processed in the HF bath followed by rinse and dry. This process sequence was repeated, without chemical changeout, for 23 test runs. The chemical process time of each run was 25 minutes, which was estimated to have etched the oxide of 560 Å in thickness in a fresh HF bath. The actual etch rate for each run, however, was obtained using an ellipsometer to measure the oxide thickness change of the test wafers. The control system was activated to let the system spike whenever needed.
RESULTS AND DISCUSSION
Results showed that the etch by-products have an effect on the linear dependence of conductivity and HF concentration. Since HF is consumed in the reaction, one would expect that the conductivity should drop as wafers are introduced into the bath. However, it was observed that conductivity increased with the amount of SiO2 etched in the bath while the oxide etch rate drops as shown in figure 1.
Fig 1
This observation was again confirmed by immersing 50 oxide wafers (8”) in the 0.5% HF bath for an extended time and monitoring the change in conductivity as illustrated in Fig. 2. The amount of SiO2 can be calculated and the relationship of conductivity versus the amount of dissolved SiO2 can be simulated as shown in Fig. 3.
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