Quantitative Analysis and Comparison of Endpoint Detection Based on Multiple Wavelength Analysis

1. Quantitative Analysis and Comparison of Endpoint Detection Based on Multiple Wavelength Analysis Abstract authors: H. H. Sawin, D. S. Boning, et al MIT Presented by Zhenwei Hou

2. Endpoint Detection • The endpoint detection is referred to the plasma diagnostic technique in the determination of the etch end point for a given process. • The endpoint detection is used for process monitoring and provide information on the types of species present in a reactive ion etching plasma, their concentration, their energy content.

3. Optical Emission Spectroscopy (OES) • OES is the most widely used technique for etch end point detection. • The change in emission from a characteristic species is observed as etching of a film is completed. • The sensitivity of this technique depends on how much etchant is consumed or how much film material is etched per unit time. ICP etcher with full wafer interferometer (FWI) and OES diagnostics

4. Material Etchant Emitting Species Wavelength (nm) Silicon CF4/O2; SF6 CF4/O2; SF6 Cl2; CCl4 F (Etchant) SiF (Product) SiCl (Product) 704 440; 777 287 SiO2 CHF3 CO (Product) 484 Si3N4 CF4/O2 CF4/O2 CF4/O2 N2 (Product) CN (Product) N (Product) 337 387 674 W CF4/O2 F (Etchant) 704 Al CCl4; Cl2; BCl3 CCl4; Cl2; BCl3 Al (Product) AlCl (Product) 391; 394; 396 261 Resist O2 O2 O2 O2 O (Etchant) CO (Product) OH (Product) H (Product) 777; 843 484 309 656 Common optical emission lines used for endpoint detection Source: Handbook of Plasma Processing Technology: Fundamentals, Etching, Deposition, and Surface Interactions. P226

5. Traditional optical emission lines used for endpoint detection • Traditional OES has been used for endpoint detection by monitoring the emission intensity from one or two wavelengths corresponding to a product or reactant species in the plasma etcher during the etch resulting in an endpoint trace. • The traditional endpoint detection method was proved unreliable to low open area (<1%) etching process such as contact and via etch, due to low signal-to-noise ratio.

6. Multi-wavelength OES endpoint detection • The new method is intended to monitor multi-wavelength to detect the endpoint. • Noise sources, either uncorrelated noise arising at the sensor or correlated process variations, need to be removed for sensitive endpoint detection. SNR = / . • The new OES endpoint detection is composed of • OES, multi-wavelength detection to improve the sensitivity of endpoint detection by a factor of 5-6 over the traditional single wavelength method; • Multivariate statistic weighted by signal-to-noise ratio (MNS) to optimize endpoint detection sensitivity.

7. Multi-Wavelength OES • Multi-wavelength OES use a dispersion grating to separate light collected from the plasma onto a linear over 1000-pixel CCD (or diode) array with a resolution of 5-10 A per pixel.

8. Multivariate Algorithms • Multivariate algorithms can be classified into one of two categories: • Mean shift in the data between the main etch and endpoint. • Covariance shift – variance structure or noise in the data between the main etch and endpoint. • The research work focuses on the mean shift algorithms are more sensitive. • Frequency-based filters can be used for removing each of these types of noise.

9. Quantifying Endpoint Sensitivity • Calculate SNR = / for each possible wavelength; • Find optimum single wavelength with the highest endpoint detection sensitivity; • Create an optimal weighting of wavelength channels to maximize the SNR improvement. Quantification of endpoint sensitivity

10. Uncorrelated Noise – Sensor Noise

11. Correlated Noise – Process Variations

12. Summary of Endpoint Detection Results / Scheme for Optimally Detecting Endpoint