Abstract
A computer-implemented method for the simulation of an energy-filtered ion implantation (EFII) is provided, including: Determining at least one part of an energy filter; determining at least one part of an ion beam source; determining a simulation area in a substrate: implementing the determined at least one part of the energy filter, the determined at least one part of the ion beam source, the determined simulation area in the substrate; Determining a minimum distance between the implemented at least one part of the energy filter and the implemented substrate for enabling a desired degree of a lateral homogenization of the energy distribution in a doping depth profile of the implemented substrate; determining a maximum expected scattering angle of the energy filter by simulating an energy-angle spectrum for the energy filter; and defining a total simulation volume.
Claims
1. A computer-implemented method for the simulation of an energy-filtered ion implantation, comprising: determining at least one part of an energy filter; determining at least one part of an ion beam source; determining a simulation area in a substrate; implementing the determined at least one part of the energy filter, the determined at least one part of the ion beam source, the determined simulation area in the substrate; determining a minimum distance between the implemented at least one part of the energy filter and the implemented substrate for enabling a desired degree of a lateral homogenization of the energy distribution in a doping depth profile of the implemented substrate; determining a maximum expected scattering angle of the energy filter by simulating an energy-angle spectrum for the energy filter; and defining a total simulation volume.
2. The method of claim 1, wherein the minimum distance between the energy filter and the substrate is between 100 ?m and 1000 ?m for the simulation of the EFII process of Al-ions with a kinetic primary energy of 12 MeV.
3. The method of claim 1, wherein the energy filter is constructed from single filter unit cells, wherein the single filter unit cells are composed of a plurality of base elements of different geometry, different material composition or different layer structure, and wherein the total width of the energy filter by the determined maximum expected scattering angle is the number of the filter unit cells arranged next to each other.
4. The method of claim 1, wherein either direction of the simulation area to be analyzed in the substrate is between 1 ?m and 500 ?m when viewed perpendicular to an ion beam.
5. A computer-implemented method for the simulation of an energy-filtered ion implantation comprising the steps of: approximating an energy filter in at least one base element; selecting at least one of the at least one base element such that the desired geometry and material composition of the energy filter to be simulated can be assembled from the selected base elements; determining the energy-angle spectrum for the selected at least one base element; determining a virtual ion beam source based on the determined energy-angle spectrum of the selected at least one base element; and simulating implantation effects in a simulation area in a substrate.
6. The method of claim 5, wherein the at least one base element is one of at least one part of at least one energy filter element, a filter unit cell of the energy filter, or a set of discrete energy filters.
7. The method of claim 5, wherein the energy filter is triangular-shaped, pyramid-shaped, inverted pyramid-shaped, or free-form shaped.
8. The method of claim 6, wherein the filter unit cell of the energy filter is composed of a plurality of base elements of different geometry, different material compositions of different layer structures.
9. The method of claim 5, wherein the implantation effects comprise at least one of defect generation, doping profile, masking effects.
10. The method of claim 5, wherein for a new filter geometry, a new filter material selection, a new layer composition of the energy filter, a new primary ion, a new primary ion energy, a new primary ion implantation angle and a new virtual ion beam source are determined.
11. The method of claim 5, further comprising the step of storing the least one base element in a data base.
12. The method of claim 5, further comprising the step of storing the virtual ion beam source in a data base.
13. The method of claim 5, further comprising the step of parametric analyzing a masking structure on the substrate for optimization of the masking thickness, material composition and masking layout and for optimizing the 3D dopant profile in the substrate.
14. The method of claim 13, wherein the analyzing of the masking structure on the substrate for optimization of the masking thickness, material composition and masking layout and for optimizing the 3D dopant profile in the substrate is using a Monte Carlo simulation.
15. A computer program, comprising instructions, which when executed out by a computer, causing the computer to carry out the method of claim 1.
Description
DESCRIPTION
[0056] The disclosure will now be described on the basis of figures. It will be understood that the aspects of the disclosure described in the figures are only examples and do not limit the protective scope of the claims in any way. The disclosure is defined by the claims and their equivalents. It will be understood that features of one aspect of the disclosure can be combined with a feature of a different aspect of the disclosure. This disclosure becomes more obvious when reading the following detailed descriptions of some examples as part of the disclosure under consideration of the enclosed drawings, in which:
[0057] FIG. 1 shows the principle of the ion implantation device with an energy filter as disclosed in the related art.
[0058] FIG. 2A shows a structure of the ion implantation device with the energy filter.
[0059] FIG. 2B shows the typical installation of an energy filter in a system for ion implantation for the purpose of wafer processing, with movable substrate.
[0060] FIGS. 3A and 3B show the typical installation of an energy filter in a system for ion implantation for the purpose of wafer processing.
[0061] FIGS. 4A to 4D show three-dimensional structures of filters illustrating the principal possibilities of using energy filters to generate a large number of doping depth profiles.
[0062] FIG. 5A shows the schematic illustration of a unit cell of a filter structure.
[0063] FIG. 5B shows the cross-sectional view of FIG. 5A in the y-z plane of a static irradiation situation of an energy filter, ion source and a substrate.
[0064] FIG. 6A shows an arrangement such that an energy filter is in contact with a substrate.
[0065] FIG. 6B shows an arrangement of an energy filter and a substrate with sufficient distance.
[0066] FIGS. 7A to 7C show a filter and substrate spaced 20 ?m, 500 ?m, and 3000 ?m apart.
[0067] FIG. 8 shows a schematic illustration of a static simulation model of an energy-filtered ion implantation EFII according to the first aspect of the present disclosure.
[0068] FIG. 9 shows a flowchart of the method according to the first aspect of the present disclosure for the simulation of the energy-filtered ion implantation (EFII).
[0069] FIG. 10 shows a schematic side view of an energy filter to be simulated.
[0070] FIGS. 11A to 11D show a schematic side views of base elements approximated from the energy filter to be simulated.
[0071] FIG. 12 shows a schematic side view of a complex energy filter to be simulated with a filter unit cell.
[0072] FIGS. 13A and 13B show simulation models of an energy-filtered ion implantation EFII according to the second aspect of the present disclosure.
[0073] FIG. 14 shows a flowchart of the method according to the second aspect of the present disclosure for the simulation of the energy-filtered ion implantation (EFII).
[0074] FIGS. 15A to 15C show an optimized parametric simulation to another aspect of the present disclosure.
[0075] FIG. 16 shows the energy-angle distribution for EFII of an Al ion with initial energy of 12 MeV and typical filter dimensions, wherein the maximum scattering angle ? is about 70?.
DETAILED DESCRIPTION
[0076] FIG. 8 shows a schematic illustration of a static simulation model of an energy-filtered ion implantation (EFII) according to the first aspect of the present disclosure. The energy-angle distribution characteristics of the static simulation model of the EFII correspond to the real implantation conditions as well as representation of the required geometric boundary conditions for correct reproduction of the energy-angle spectrum. FIG. 8 shows the dimensioning of the ion source resulting from the area-wide scanning process of the ion beam during implantation. The EFII process can be simulated into a Monte Carlo simulation environment. As can be seen in FIG. 8, the filter-substrate distance 50 (fs) has to be dimensioned at least with the distance 50 in which the scattering of the ions leads to a desired degree of a lateral homogenization of the energy distribution and in one aspect implementation no structure transfer of the microstructure of the energy filter into the substrate 26 is visible. For a simulation of the EFII process of Al ions with a kinetic primary energy of 12 MeV, this minimum distance 50 is fs=500 ?m according to FIG. 7B.
[0077] The maximum expected scattering angle ? of a filter unit cell 30 is to be determined. For this purpose, the energy-angle spectrum for a given filter unit cell 30 is simulated and the maximum scattering angle ? (which is still experienced by a relevant number of ions) is determined. In particular, with a high number of simulated ions, there will always be a few ions that have scattering angles close to 90?. Therefore, the angle ? could be defined in a way that the angle ? includes the relevant part of the scattered ions, i.e., not considering the scattered ions with a scattering angle larger than the angle ?, which in total make up less than 1% or 2% of the total number of ions. This lowers the accuracy but simplifies the simulation. As shown in FIG. 8, this maximum angle ? is used to calculate the total width of the filter model, i.e., how many filter elementary cells must be arranged next to each other in a side-by-side manner. To guarantee that the full angle spectrum of the ions will hit the simulation area g, the characteristic energy filter implantation profile will be generated in the simulation area g. For the simulation of the EFII process of Al ions with a primary kinetic energy of 12 MeV and a distance 50 between the energy filter 25 and the substrate 26 of fs=500 ?m, the maximum scattering angle ? is about 70? degrees, as can be seen in FIG. 16. FIG. 8 shows the width of the energy filter 25 and the ion source 5. The area to be analyzed (i.e., simulation area g) in the substrate 26 is given by g=2 ?m. The required total width of ion beam source 5 and the energy filter 25 is thus L=2749 ?m. The area to be analyzed (i.e., simulation area g) in the substrate 26 can also be between 1 ?m and 500 ?m.
[0078] The total dimension of the simulation is calculated with the formula L=l+g, wherein the width l of the ion beam source 5 and the energy filter 25 is calculated with, the following formula:
?=f.sub.s tan(?)
wherein ?=the maximum scattering angle, and f.sub.s=distance 50 between the energy filter 25 and the substrate 26.
[0079] FIG. 9 shows a flowchart of the computer-implemented method 200 according to the first aspect of the present disclosure for the simulation of the energy-filtered ion implantation (EFII). The method 200 for the simulation of an energy-filtered ion implantation (EFII) comprising the steps of: determining 201 at least one part of an energy filter 25; determining 202 at least one part of an ion beam source 5; determining 203 a simulation area g in a substrate 26; implementing 204 the determined at least one part of the energy filter 25, the determined at least one part of the ion beam source 5, the determined simulation area g in the substrate 26. The simulation environment is, for example, a Monte Carlo simulation. The method 200 further comprises the step of determining 205 a minimum distance 50 (fs) between the implemented at least one part of the energy filter 25 and the implemented substrate 26 for enabling a desired degree of a lateral homogenization of the energy distribution in a doping depth profile 40 of the implemented substrate 26; determining 206 the maximum expected scattering angle ? of the energy filter 25 by simulating an energy-angle spectrum for the energy filter 25; and defining 207 the total simulation volume S.sub.v (see dotted line in FIG. 8).
[0080] For example, the method 200 further requires that the minimum distance 50 (fs) between the energy filter 25 and the substrate 26 is between 100 ?m and 1000 ?m for the simulation of the EFII process of Al-ions with a kinetic primary energy of 12 MeV. The method 200 further comprises that the maximum expected scattering angle ? of the filter unit cell 30 is 70? for the simulation of the EFII process of Al-ions with a kinetic primary energy of 12 MeV and the minimum distance 50 of 500 ?m. The method 200 further comprises that the simulation area g in the substrate 26 is in one-dimension 2 ?m. The method 200 further comprises that the total width l of the energy filter 25 by the determined maximum expected scattering angle ? is the number of filter unit cells arranged next to each other. For example, the minimum distance 50 (fs) between the energy filter 25 and the substrate 26 can also be 0 ?m (no homogenization) over 100 ?m up to 1000 ?m or up to some millimeters (full homogenization). For light ions (hydrogen) and very high energies and large filter structures (e.g., 100 ?m thickness) larger distances 50 than 1000 ?m will be necessary.
[0081] FIG. 10 shows a schematic side view of the energy filter to be simulated. The computer-implemented method 300 according to the second aspect of the present disclosure comprises a first process step of selecting one or more base elements 25a-1, 25a-2, . . . 25a-n. The base elements 25a-1, 25a-2, . . . 25a-n are for example energy filter elements 25a (shown in FIG. 11A), filter unit cells 30 or a set of discrete energy filter elements 25a (shown in FIG. 11C). The selection is made in such a way that the desired geometry and material composition of the overall energy filter 25 to be simulated can be assembled from these base elements. FIGS. 11A to 11D show examples illustrating the possibilities of selecting or defining base elements 25a-1, 25a-2, . . . 25a-n. FIGS. 11A to 11D show a schematic side views of the base elements 25a-1, 25a-2, . . . 25a-n approximated from the energy filter 25 to be simulated. FIG. 12 shows a schematic side view of a complex energy filter to be simulated with a filter unit cell 30. As shown in FIG. 11D, the triangular structure of the energy filter elements 25a can be approximated by n-adjacent discrete filter membrane pieces 25a-1, 25a-2, 25a-3, . . . 25a-n.
[0082] FIGS. 13A to 13B show a simulation model of an energy-filtered ion implantation EFII with approximated geometric conditions according to the second aspect of the present disclosure. FIGS. 13A and 13B show the schematic representation of the geometric simulation models of the method 300 as well as the sequence of the simulations. However, the present disclosure is not limited to a sequence of the simulations but could also be a single simulation.
[0083] As shown in FIG. 14, the method 300 according to the second aspect of the present disclosure for the simulation of the energy-filtered ion implantation (EFII) with approximated geometric conditions, comprises as a first process step the steps of: approximating 301 the energy filter 25 in at least one base element 25a-1, 25a-2, . . . , 25a-n; selecting 302 at least one of the at least one base element 25a-1, 25a-2, . . . , 25a-n such that the desired geometry and material composition of the energy filter 25 to be simulated can be assembled from the selected base elements 25a-1, 25a-2, . . . , 25a-n; and determining 303 the energy-angle spectrum for the selected at least one base element 25a-1, 25a-2, . . . , 25a-n. The at least one base element 25a-1, 25a-2, . . . , 25a-n is one of at least one part of at least one energy filter element 25a, a filter unit cell 30 of the energy filter 25, or a set of discrete energy filters 25. The energy filter 25 can be, for example, triangular-shaped, pyramid-shaped, inverted pyramid-shaped, or free-form shaped. The filter unit cell 30 of the energy filter 25 is composed of a plurality of base elements of different geometry, different material compositions of different layer structures.
[0084] After the first process step, in the next step, the relevant properties of the ion beam characteristics (energy and angle, y-z coordinates dependency of energy and angle of the ions), which act on the simulation area g due to the filter properties and the properties of the primary ions, are calculated for all of the selected basic elements 25a-1, 25a-2, . . . , 25a-n. The method 300 according to the present disclosure is not limited to triangular-shaped ones of the energy filters 25. Rather, pyramidal, inverted pyramidal, or more generally free-form structures for the energy filters 25 can also be simulated using the method 300. For example, the energy filter 25 or filter unit cell 30 can be composed of a plurality of base elements 25a-1, 25a-2, . . . , 25a-n of different geometry, different material composition or different layer structure. Tilting of the energy filter 25 or mirroring about an axis perpendicular to the ion beam 10 is also possible.
[0085] As shown in FIG. 14, after the first process step, the method 300 for the simulation of the energy-filtered ion implantation (EFII), comprising as a second process step the steps of: determining 304 a virtual ion beam source 5 based on the determined energy-angle spectrum and defining for a EFII desired degree of lateral (y-z coordinates) homogenization of distribution of energy and angles of the ions of the selected at least one base element 25a-1, 25a-2, 25a-3, . . . , 25a-n. In one further aspect, the energy-angle distribution of a single energy filter 25 is determined, where the energy filter 25 can be a simple elementary cell, as in FIG. 10, or a complex elementary cell, as in FIG. 12. The determination of the energy-angle distribution can be done by a plurality of steps using different methods. Simulation methods (simulation of one or more basic elements), analytical methods as well as experimentally obtained results or combinations of such methods are conceivable.
[0086] The method 300 for the simulation of the energy-filtered ion implantation (EFII), comprising as the second process step also the step of simulating 305 the implantation effects in the simulation area g in the substrate 26. In the next step, a virtual ion beam source 5 with an energy-angle characteristic is defined, which is composed of the ion beam characteristics of the base elements 25a-1, 25a-2, 25a-3, . . . , 25a-n selected in the first process step of method 300. Thus, this composite virtual ion beam source 5 corresponds exactly (or approximates) the ion beam characteristics (energy and angle distribution) of the overall energy filter 25 to be simulated. As shown in FIGS. 13A and 13B, this virtual ion source 5, also referred as EFIIS source 5, is used to simulate the implantation effects (defect generation, doping profile, masking effects) in the target substrate 26 under investigation (simulation region g).
[0087] Therefore, for each new filter geometry, new filter material selection, new layer composition of the energy filter 25, new primary ion, new primary ion energy, new primary ion implantation angle (i.e., distribution) a new virtual ion beam source 5, i.e., EFIIS source 5, is defined. The ion beam source 5, i.e., EFIIS source 5, can be used to simulate and analyze the effects of ion implantation on any substrate 26. The ion beam source 5, i.e., EFIIS source 5, and also the underlying base elements 25a-1, 25a-2, 25a-3, . . . , 25a-n of the energy filters 25 can be stored in databases (not shown) in the first process step of the method 300. Furthermore, it is possible to successively improve the virtual ion beam source 5, i.e., EFIIS source 5, by matching simulation results with experimental results.
[0088] As shown in FIG. 14, the method 300 further comprises that the implantation effects are one of defect generation, doping profile, masking effects. The method 300 further comprises that for a new filter geometry, a new filter material selection, a new layer composition of the energy filter 25, a new primary ion, a new primary ion energy, a new primary ion implantation angle, and a new virtual ion beam source 5 is determined. The method 300 further comprises the step of storing 306 the least one base element 25a-1, 25a-2, 25a-3, . . . , 25a-n in a data base (not shown). The method 300 further comprises the step of storing 307 the virtual ion beam source 5 in a data base (not shown).
[0089] Further significant advantages result from a systematic investigation of a simulation area g, with variation of a geometry parameter in the simulation area. This is shown, for example, in FIGS. 15A to 15C, which illustrates a typical simulation investigation when varying the masking thickness on the substrate 26. The aim of this investigation is to determine the necessary masking thickness, geometrical shape, material composition and layout and for investigating/optimizing the 3D dopant profile in the substrate for a fixed energy filter 25 and fixed primary ion properties, i.e., for a given ion beam source, i.e., EFIIS source, using a Monte Carlo simulation. By separating the first process step and the second process step according to method 300 as well as the constant EFII parameters, the first process step only needs to be performed once and second process step for each variation of the masking thickness. This saving of the process step for each follow-up investigation of the second process step is reflected in a saving of simulation time. Library solutions are also conceivable, in which the ion beam characteristics are stored with defined parameters for follow-up simulations in the Monte Carlo simulation. These simulation optimizations have a positive effect on simulation durations, hardware, resources, and energy consumption.
[0090] As shown in FIG. 14, the method 300 further comprises the step of parametric analyzing 308 a masking structure 70 on the substrate 26 for detecting the masking thickness geometrical shape, material composition and layout and for investigating/optimizing the 3D dopant profile in the substrate, as shown in FIGS. 15A to 15C. As shown in FIG. 14, the method 300 further comprises that the analyzing 308 of the masking structure 70 on the substrate 26 is carried out by using a Monte Carlo simulation.
[0091] As shown in FIG. 16, the energy-angle distribution for EFII of an Al ion with initial energy (E) of 12 MeV and typical filter dimensions, wherein the maximum scattering angle ? is about 70?.
