Jafar Al-Kuzee

Hairpin resonator probe measurements in RF plasmas

This work investigates the use of hairpin probes in plasma where RF plasma potential is present. The microwave resonance of the hairpin is used to determine electron density. Two types of hairpin probe were used. One type was dc coupled: its dc potential could be varied while monitoring its resonance frequency and collected current. The other probe was designed to be fully floating, being (dc) isolated from ground and able to float with RF variations in the plasma potential. Additional measurements of the RF plasma potential and its effect on the dc floating potential of the former probe were made using a wire loop probe. The resonant frequency of the dc coupled probe at zero current (nominal floating potential) was less than that determined from the fully floating probe. This is attributed to the wider sheath around the former caused by RF plasma potential across it. The presence of the electron-free sheath around the wires of the hairpin is included in the analysis that links the resonant frequency to the electron density in the bulk plasma. When the dc coupled probe was biased at the true floating potential (determined from independent loop probe measurements) its resonant frequency was closer to, though still consistently higher than, that of the floating probe. This work shows that RF potential across the probe sheath affects the resonance of a hairpin probe and should be accounted for when using hairpin probes in discharges where RF plasma potential variations are even as low as a few times the electron temperature (in volts).

Electron and ion sheath effects on a microwave “hairpin” probe

A microwave measurement of electron density in low-pressure plasmas can be based on a hairpin probe. The hairpin forms a transmission line that supports a quarter-wavelength standing wave. The resonance is related to the relative permittivity of the surroundings, and hence, in a plasma, electron density can be evaluated. For improved fidelity, a general model is developed to include the effects of positive and negative space-charge sheaths formed around the hairpin wires. The former tends to lower the resonance, whereas the latter tends to raise it initially. This is qualitatively in agreement with experiments in dc argon plasmas.

Optimization of plasma etch processes using evolutionary search methods with in situ diagnostics

This paper presents several approaches that have been used to control, optimize and characterize a low pressure (10–300 mTorr) plasma processing system. Methods such as contour following and differential evolution have been used to find contours of DC bias, total ion flux, ion energy flux, quadrupole mass spectrum (QMS) intensity ratios and line intensity ratios of the optical emission spectrum (OES) in argon and nitrogen plasmas. A mapping for a 4 × 4 multi-dimensional parameter space is also presented, in which the relationship between four control parameters (power, pressure, mass flow rates of two supplied gases) and four measurement outputs (DC bias, ion flux, QMS ratios and OES line intensity ratios) is determined in a plasma etching process. The use of these methods significantly reduces the time needed to re-configure the processing system and will benefit transfer of processes between different systems. A similar approach has also been used to find quickly an optimum condition for directional etching of a silicon wafer.

Intelligent control of low pressure plasma processing

Several parameters characterize systems for materials processing that use radio frequency electrical discharges in gases at low pressure. These include directly measurable quantities such as a DC bias voltage, an ion current, an energy flux, masses of charged species, and spectrally resolved optical emission. None of these is directly controllable but all are dependent on several variables that can be controlled such as radio-frequency (RF) power, chamber pressure, and gas flow rates. There is a rich parameter space that must be painstakingly searched for optimum conditions for any particular process. In place of the relatively slow manual procedure, an artificial intelligence (AI) approach has been used to map out contours for all of the above characteristic parameters in the control space. Automatic characterization of plasma systems in this way could significantly reduce the time to re-configure them and to transfer processes between different systems.