3D Simulation of Ion Milling for Mass Storage Applications

Z. Djuric, A. Hoessinger, A. Babayan, A. Nejim, Silvaco Data Systems Europe Ltd,
Silvaco Technology Centre, Compass Point, St. Ives, Cambridgeshire PE27 5JL UK,
B. Lafferty & A. Moore, Seagate Technology (Ireland), 1 Disc Drive, Springtown Ind Estate,
Derry, N. Ireland BT48 0BF, UK
M.A. Seigler, Seagate Research, 1251 Waterfront Place, Pittsburgh, PA 15222, USA

 

Introduction

The ion milling process is used extensively in the Hard Disc Drive industry, particularly in the manufacture of thin film magnetic heads. Ion milling is used to pattern many metal and dielectric materials including alloys comprising of Fe, Co and/or Ni transition metals which are commonly found in a thin film magnetic read-write transducers. This paper presents new results for ion milling and redeposition of gold on photoresist patterns at different milling angles and compared with 3D process simulation results.

 

Ion Milling Process

The basic principle of ion milling involves bombarding a target with energetic ions or neutral atoms accelerated and formed into an ion beam [1, 2]. Material is sputtered from the specimen resulting in thinning of sheet film samples or pattering selectively masked structures. The etch rate is usually highly sensitive to mill angle and is dependant upon the element or alloy being sputtered. Argon or another inert element is typically used for the milling beam. A reactive element can also be introduced into the plasma to adjust the relative selectivity or etch rate of materials being removed.

 

3D Process Simulation Model

To accurately reproduce such a process in 3D, a novel algorithm with a stable and robust representation of the evolving geometry and an accurate multilayer representation has been implemented in Silvaco VICTORY PROCESS software [3]. This generic 3D process simulator offers objective adaptive meshing to automatically resolve smaller geometries and C-functions to incorporate user defined physical models.

The ion mill yield as a function of beam angle to the surface normal with rotation has been measured for planar substrates at Seagate (Figure 1). Apart from the experimental setup of the initial structure geometry and the beam angle, the data obtained from these basic measurements were the only input needed for the simulation software.

Figure 1. Etch rates as a function of the angle between the ion mill beam and the normal to the surface for various materials considered in this study.

 

Three important factors are considered in simulating the ion milling of any surface segment:

First the direct etch rate due to the primary flux (ion beam), expressed as a function of impact angles and other parameters is taken into account. Several flux functions have been implemented via the C-Interpreter interface of VICTORY PROCESS. The most comprehensive describes a rotating ion beam (equivalent to a rotating substrate) with a divergent ion flux. A number of feature combinations have been implemented to reduce calculation time if, for instance, the beam divergence is neglected, or if the beam is not rotating. The divergent static beam is modelled via a 2D von Mises distribution function while a beam rotation is modelled by removing the ϑ dependence from the von Mises function [4].

A secondary flux caused by the redeposition of primary material has been considered (Figure 2).

Figure 2. Au redeposition rate as a function of the angle between the ion mill beam and the normal to the surface of Gold.

 

To predictively account for the angular dependence [5] of the process, the milling efficiency (efficiency as a function of angle of incidence) has been measured for several materials. A table based representation of these functions is part of the C-Interpreter models.

The final local etch rate, is affected by two competing processes of ion milling and re-deposition at each surface point. The effective etch rate is calculated for all surface points using:

Rtot is the effective local milling rate, S is the surface reaction function, Fprim is the primary flux function (direct ion flux), Fredep is the re-deposition flux coming from other parts of the surface (depends on previously calculated Fprim). Note that the total rate Rtot can be negative (when the surface is etched away) or positive (when the flux of re-deposited material exceeds the etch rate).

 

Comparison to Measurements

Although the results presented in this work are essentially 2D for convenient comparison, the software performs the calculations in 3D. Apart from the milling of resist stripes, the etch effects on the corners of rectangular resist apertures have also been demonstrated.

The results shown in figures 4 and 5 illustrate a good agreement between the observed cross-sectional SEM micrographs of the resist stripes and the simulation. Some features have not been reproduced which could be due to the rounding of the initial structure as well as the divergence of the milling beam. Figure 6 shows ion milling of a resist cone with shadowing and redeposition.

 

Figure 3. SEM micrograph showing a cross section through a 500nm resist pattern line on gold before ion milling with the initial simulation outline superimposed for comparison.

 

Figure 4. SEM micrograph for the structure in Figure1 following a 60 second ion mill at 0o (i.e. vertical beam) with rotation.

 

Figure 5. SEM micrograph for the structure in Figure1 following a 60 second ion mill at 30o off the vertical with rotation.

 

Figure6 (a) A demonstration conical photoresist shape on a gold substrate. (b) shows the ion milling process for 60 seconds at 30o degrees off the vertical with a rotation (sweep) angle of 45o. (c) Obtaining a cut plane from the 3D structure. (d) a 2D cross section along the 45o rotation plane.

 

Conclusion

A 3D simulation algorithm has been implemented in VICTORY PROCESS to model ion milling of complex topographies. A reasonable match between measured ion milled resist on gold profiles and simulation results based on basic material parameters have been obtained in this study to demonstrate the predictive capability of the algorithm.

 

References

  1. S. Tumanski, Thin Film Magnitoresistive Sensors, Taylor & Francis, 1st Ed. ISBN 0750307021 (2001)
  2. J.F.O’Hanlon, A User’s guide to Vacuum Technology, John Wiley & Sons, 2nd Ed. ISBN 0-471-81242-0 (1989).
  3. V.Suvorov, A.Hossinger, Z.Djuric and N. Ljepojevic, A novel approach to three-dimensional semiconductor process simulation: Application to thermal oxidation, J. Comput. Electron. 5 291 (2006).
  4. M.Evans, N.Hastings and B.Peacock, von Mises Distribution, Ch. 41 in Statistical Distributions, 3rd Ed. New York, Wiley (2000).
  5. E.J.Klein and W.F.Ramirez, Consideration of local shadowing and ion beam voltage effects in the prediction of a surface evolving under ion milling, J. Vac. Sci. Technol A 18(1) 166 (2000).

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