Method of determining a set of metrology points on a substrate, associated apparatus and computer program

A method of determining a set of metrology point locations, the set including a subset of potential metrology point locations on a substrate, the method including: determining a relation between noise distributions associated with a plurality of the potential metrology point locations using existing knowledge; and using the determined relation and a model associated with the substrate to determine the set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the U.S. national phase entry of PCT Patent Application No. PCT/EP2020/057617 which was filed on Mar. 19, 2020, which claims the benefit of priority of European Patent Application No. 19164831.0, which was filed on Mar. 25, 2019, and of European Patent Application No. 19170764.5, which was filed on Apr. 24, 2019, which are incorporated herein in their entireties by reference.

FIELD

The present invention relates to a method of determining a set of metrology points on a substrate, an associated apparatus and a computer program.

BACKGROUND

Whichever type of apparatus is employed, the accurate placement of patterns on the substrate is a chief challenge for reducing the size of circuit components and other products that may be produced by lithography. In particular, the challenge of measuring accurately the features on a substrate which have already been laid down is a critical step in being able to position successive layers of features in superposition accurately enough to produce working devices with a high yield. So-called overlay should, in general, be achieved within a few tens of nanometers in today's sub-micron semiconductor devices, down to a few nanometers in the most critical layers.

Consequently, modern lithography apparatuses involve extensive measurement or ‘mapping’ operations prior to the step of actually exposing or otherwise patterning the substrate at a target location. These operations, being time-consuming, limit the throughput of the lithography apparatus, and consequently increase the unit cost of the semiconductor or other products.

As pattern features become smaller and overlay performance requirements become ever more demanding, so-called advanced alignment models have been and continue to be developed to model and correct more accurately non-linear distortions of the “wafer grid”. These advanced models depend on measuring an increased number of targets across the substrate. Ultimately, however only a limited number of the available targets can be measured without unduly limiting the throughput of the lithographic process as a whole.

These problems have been addressed by sampling scheme optimization methods, which select a subset of the total number of potential targets (or metrology point locations) on a substrate for use in alignment.

However, in such methods, there is an implicit assumption that the magnitude of the measurement variation is equal and uncorrelated across the substrate or within the field/image area. This is not the case in real systems. For example, the scanner behavior will correlate within the slit and the variation can change across the substrate (e.g. increasing at the edges). This causes the sampling schemes to be sub-optimal.

SUMMARY

It is therefore desirable to increase the reliability of said alignment models, or other metrology processes, without decreasing throughput by taking account of known aspects of the variations and correlations between variations. This may allow the effect of changes in the variation and/or correlations in the variation to be taken into account in the sampling scheme so that their effect on the alignment is reduced.

In one aspect, the invention provides a method of determining a set of metrology point locations, said set comprising a subset of potential metrology point locations on a substrate; wherein said method comprises: determining a relation between noise distributions associated with a plurality of said potential metrology point locations using existing knowledge; and using the determined relation and a model associated with said substrate to determine the set.

In another aspect, the invention provides a method of determining a model for fitting measurements, the model comprising a plurality of pre-determined base functions and coefficients associated with each of those base functions, the method comprising the steps of: determining a relation between noise distributions associated with a plurality of measurement positions using existing knowledge; and using the determined relation and calculated values of each base function at the measurement positions to determine the coefficients.

In further aspects, the invention provides a computer program comprising computer readable instructions which, when run on suitable computer apparatus, cause the computer apparatus to perform the method of the above aspect; a computer program product comprising such a computer program; and an apparatus having a processor specifically adapted to carry out the steps of the method of the above aspect.

These and other features and advantages of particular embodiments of the invention will be understood by the skilled reader from a consideration of the exemplary embodiments discussed below.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The lithographic apparatus may be of a type having two (dual stage) or more substrate tables (and/or two or more mask tables). In such “multiple stage” machines the additional tables may be used in parallel, or preparatory steps may be carried out on one or more tables while one or more other tables are being used for exposure. The invention disclosed herein can be used in a stand-alone fashion, but in particular it can provide additional functions in the pre-exposure measurement stage of either single- or multi-stage apparatuses.

Lithographic apparatus LA in this example is of a so-called dual stage type which has two substrate tables WTa and WTb and two stations—an exposure station and a measurement station—between which the substrate tables can be exchanged. While one substrate on one substrate table is being exposed at the exposure station EXP, another substrate can be loaded onto the other substrate table at the measurement station MEA so that various preparatory steps may be carried out. The preparatory steps may include mapping the surface of the substrate using a level sensor LS and measuring the position of alignment mark on the substrate using an alignment sensor AS. This enables a substantial increase in the throughput of the apparatus. If the position sensor IF is not capable of measuring the position of the substrate table while it is at the measurement station as well as at the exposure station, a second position sensor may be provided to enable the positions of the substrate table to be tracked at both stations. The invention can be applied in apparatus with only one substrate table, or with more than two.

The apparatus further includes a lithographic apparatus control unit LACU which controls all the movements and measurements of the various actuators and sensors described. LACU also includes signal processing and data processing capacity to implement desired calculations relevant to the operation of the apparatus. In practice, control unit LACU will be realized as a system of many sub-units, each handling the real-time data acquisition, processing and control of a subsystem or component within the apparatus. For example, one processing subsystem may be dedicated to servo control of the substrate positioner PW. Separate units may even handle coarse and fine actuators, or different axes. Another unit might be dedicated to the readout of the position sensor IF. Overall control of the apparatus may be controlled by a central processing unit, communicating with these sub-systems processing units, with operators and with other apparatuses involved in the lithographic manufacturing process.

FIG. 2illustrates the known steps to expose target portions (e.g. dies) on a substrate W in the dual stage apparatus ofFIG. 1. On the left hand side within a dotted box are steps performed at a measurement station MEA, while the right hand side shows steps performed at the exposure station EXP. From time to time, one of the substrate tables WTa, WTb will be at the exposure station, while the other is at the measurement station, as described above. For the purposes of this description, it is assumed that a substrate W has already been loaded into the exposure station. At step200, a new substrate W′ is loaded to the apparatus by a mechanism not shown. These two substrates are processed in parallel in order to increase the throughput of the lithographic apparatus. Referring initially to the newly-loaded substrate W′, this may be a previously unprocessed substrate, prepared with a new photo resist for first time exposure in the apparatus. In general, however, the lithography process described will be merely one step in a series of exposure and processing steps, so that substrate W′ has been through this apparatus and/or other lithography apparatuses, several times already, and may have subsequent processes to undergo as well.

The previous and/or subsequent processes may be performed in other lithography apparatuses, as just mentioned, and may even be performed in different types of lithography apparatus. For example, some layers in the device manufacturing process which are very demanding in parameters such as resolution and overlay may be performed in a more advanced lithography tool than other layers that are less demanding. Therefore some layers may be exposed in an immersion type lithography tool, while others are exposed in a ‘dry’ tool. Some layers may be exposed in a tool working at DUV wavelengths, while others are exposed using EUV wavelength radiation.

At202, alignment measurements using the substrate marks P1etc. and image sensors (not shown) are used to measure and record alignment of the substrate relative to substrate table WTa/WTb. In addition, several alignment marks across the substrate W′ will be measured, to establish a “wafer grid”, which maps very accurately the distribution of marks across the substrate, including any distortion relative to a nominal rectangular grid. At step204, a map of substrate height against X-Y position is measured also, for use in accurate focusing of the exposed pattern.

When substrate W′ was loaded, recipe data206were received, defining the exposures to be performed, and also properties of the substrate and the patterns previously made and to be made upon it. To these recipe data are added the measurements of substrate position, substrate grid and height map that were made at202,204, so that a complete set of recipe and measurement data208can be passed to the exposure stage. The measurements of alignment data for example comprise X and Y positions of alignment targets formed in a fixed or nominally fixed relationship to the product patterns that are the product of the lithographic process. These alignment data, taken just before exposure, are combined and interpolated to provide parameters of an alignment model. These parameters and the alignment model will be used during the exposure operation to correct positions of patterns applied in the current lithographic step. A conventional alignment model might comprise four, five or six parameters, together defining translation, rotation and scaling of the ‘ideal’ grid, in different dimensions. As described further below, advanced models are known that use more parameters.

At210, substrates W′ and W are swapped, so that the measured substrate W′ becomes the substrate W entering the exposure station EXP. This swapping is performed by exchanging the supports WTa and WTb within the apparatus, so that the substrates W, W′ remain accurately clamped and positioned on those supports, to preserve relative alignment between the substrate tables and substrates themselves. Accordingly, once the tables have been swapped, determining the relative position between projection system PS and substrate table WTb (formerly WTa) is all that is necessary to make use of the measurement information202,204for the substrate W (formerly W′) in control of the exposure steps. At step212, reticle alignment is performed using the mask alignment marks M1, M2. In steps214,216,218, scanning motions and radiation pulses are applied at successive target locations across the substrate W, in order to complete the exposure of a number of patterns. By using the alignment data and height map obtained at the measuring station in the performance of the exposure steps, these patterns are accurately aligned with respect to the desired locations, and, in particular, with respect to features previously laid down on the same substrate. The exposed substrate, now labeled W″ is unloaded from the apparatus at step220, to undergo etching or other processes, in accordance with the exposed pattern.

Current standard alignment models may comprise six parameters (effectively three per direction X and three per direction Y). This may be adequate for some applications, but for more demanding processes a more detailed correction of the wafer grid may be required to achieve a desired overlay performance. Advanced alignment models have been developed for this purpose. In this text, the term “advanced alignment models” is used to refer to models having greater complexity than the standard six parameters. While simpler models might use fewer than ten parameters, advanced alignment models typically use more than 15 parameters, or more than 30 parameters. Examples of advanced models are higher order wafer alignment (HOWA) models, zone-alignment (ZA) and radial basis function (RBF) based alignment models. HOWA is a published technique based on third and higher order polynomial functions. Zone alignment is described for example in Huang et al, “Overlay improvement by zone alignment strategy”, Proc. SPIE 6922, 69221G (2008). Different versions and extensions of these advanced models can be devised. The advanced models generate a complex description of the wafer grid that is corrected for, during the exposure of the target layer. RBF and latest versions of HOWA provide particularly complex descriptions based on tens of parameters. This implies a great many measurements are required to obtain a wafer grid with sufficient detail.

Even in embodiments with multiple substrate tables WTa/WTb, the time taken to obtain sufficient measurements for advanced alignment on each substrate eventually impacts throughput. Reducing the time per measurement tends to decrease the accuracy of each measurement, so that the impact on throughput is hard to avoid. In addition, once corrections have been applied in one layer using an advanced alignment model, the same level of detail should be applied in subsequent layers, or the corrections in the first layer become a source of error in the overlay of subsequent layers. The manufacturer therefore has a difficult choice whether to accept further measurement overhead by using the advanced model in subsequent layers, or to suffer an overlay penalty by reverting to a simpler alignment model in subsequent layers, measuring fewer marks.

There is a large degree of similarity in the problems faced for alignment and model estimation/correction calculation. The commonality is that a certain systematic pattern is estimated using a limited set of measurements taken at certain locations. The positions from which measurements are selected for inclusion in the estimation process, determines how reliable the resulting model is. This is because not all measurement positions are necessarily equally informative for the estimation procedure.

Current customer high volume manufacturing (HVM) measurement schemes typically sample four to eight fields on the substrate densely while covering the rest of the substrate in a sparse fashion (e.g. one metrology point per field). This is already suboptimal for the models currently in use, and more serious problems begin to occur for higher order models.

In a lithographic apparatus such as shown inFIG. 1, alignment is performed for each substrate prior to exposure. Multiple metrology points (e.g., alignment marks) are used to capture the shape of the substrate and to average out placement noise (e.g., originating from the lithographic apparatus baseline). Typically a subset of metrology point locations are selected for sampling from a number of possible metrology point locations on a substrate, the subset of metrology point locations comprising far fewer locations compared to the number of possible metrology point locations. Current algorithms for alignment determine which metrology point locations are selected for sampling based on covering the substrate in a uniform fashion, uniformity being defined as being equal or approximately equal distances between neighboring metrology points.

Various methods exist for determining the metrology point locations on the substrate which are selected for the sampling process. One such method is set out in U.S. Pat. No. 9,811,006B2, in which a range of potential metrology points are evaluated and selected using a measurement scheme optimization algorithm.

A criterion used in the evaluation of whether a metrology point location should be selected for inclusion in the sampling process may be D-optimality. In D-optimal designs, the determinant of the information matrix is maximized (and hence the determinant of the variance covariance matrix is minimized). An example for illustration is provided below.

Assuming a linear model, that is a model which is linear in its parameters, the following equation can be written:

[m1m2⋮mn]=[C11C12…C1⁢qC21C22…C2⁢q⋮⋮⋱⋮Cn⁢⁢1Cn⁢⁢2…Cnq]·[p1p2⋮pq]+[ξ1ξ2⋮ξn]
Measurements are denoted by m, parameters by p, residuals by ξ, and the so-called design matrix by C. This design matrix forms the heart of the model, and it is comprised of the basis functions evaluated at selected metrology locations where the respective measurements were taken. Using for example a one dimensional polynomial model in x of orders zero through four, the basis functions would simply be; 1, x, x2, x3and x4respectively. Therefore, if a measurement were to be available for location x=3, not taking into account normalization, the corresponding row in C would be: [1 3 9 27 81].

The modeling process may then proceed as follows:1. measurements at selected metrology point locations are (made) available;2. a suitable model form (i.e. a set of basis functions) is chosen so as to capture the relevant information underlying the data;3. a minimization is performed yielding parameter values which minimize the distance in some mathematical norm between the model and the measurement data, this minimization may take the form of a least squares modeling.

Keeping the same notation as in the above, the optimization problem solved in least squares estimation is as follows:

Which in turn can be solved as follows:

CTC is the information matrix, and its inverse [CTC]−1is the variance-covariance matrix. The information matrix and variance-covariance matrix both indicate how informative the measurement scheme (i.e. the experiment) is for the chosen model; i.e., how well the measurement scheme will allow differentiation of the parameters (it should be noted that actual measurement values are not used for this). So, minimizing the determinant of the variance-covariance matrix or maximizing the determinant of the information matrix will yield the same result.

The D-optimality approach is targeted to minimize the volume of uncertainty associated with the coefficient of the model that the user wants to apply to fit the measurement results. Therefore a sampling scheme optimization approach using D-optimality provides a sampling scheme optimal for a particular pre-defined model. However, this assumes that the metrology point locations are mutually uncorrelated.

When it is known that the metrology point locations are not statistically uncorrelated and/or the variance of the noise is not uniform, the covariance matrix expressing the mutual correlation is not an identity matrix and it is therefore not the regular design matrix that needs to be taken into account when choosing the sampling scheme.

Accordingly, rather than considering the information matrix CTC (or the variance-covariance matrix [CTC]−1), the sampling scheme optimization approach of an embodiment of the present invention seeks to minimize the determinant of the matrix M defined as follows:
M=(CT·Σ−1·C)−1
wherein Σ is a co-variance matrix which incorporates knowledge of the variation behavior and correlation. The covariance matrix is constructed using knowledge of the variation behavior which may be obtained from one or more of a range of sources such as dense on-product measurements or scanner measurement data.

Using such knowledge improves the sampling scheme optimization by allowing it to put more weight (i.e. extra target density) in areas which show stronger variation, such as the edge of the substrate. It can also improve the sampling scheme optimization by taking account of repetitive features, such as the scanner behavior correlating within the slit.

A covariance matrix which takes account of correlation such as that used in the embodiments above can be constructed in a variety of ways, depending on the knowledge of the correlation available.

One method of constructing the covariance matrix is to use the maximum likelihood estimator for the covariance matrix, Σ. Let X∈Mn×pbe the matrix with the measurement data, were the first index runs over all measurements for a single substrate, a total of n measurements, and the second index p over all the measured substrates. Now it is possible to estimate Σ by

Unfortunately, in order to get a good estimate, this would require p>>n, which is typically not feasible at the consumer level.

Therefore, it is preferable to incorporate domain knowledge into the construction of the covariance matrix. Again, this can be done in several ways.

One approach, according to an embodiment of the present invention, is to perform a cascade subspace approach as set out below.

First the average field per substrate is calculated, and this average field is mapped to the full layout, giving Xavgf. From this the maximum likelihood covariance estimator: Savgf∈Mn×ncan be calculated for the average field using the method set out above. This describes the average field effects, such as reticle align errors.

On the residual, i.e. X−Xavgf(all substrates with their average field removed), we can now project to a typical inter-field model set, HOWA3 e.g., giving Xinter∈Mn×pand its maximum likelihood covariance estimator: Sinter. This describes the inter-field effects, such as substrate alignment errors.

On the further residual of that, i.e. Xres:=X−Xavgf−Xinter, it is then possible to ‘stack the fields’, that is reshape the matrix such that the each columns represent an exposure field,

Xres,stacked∈Mq×(p·nq),
where q is the number of marks per field, giving an estimate Sstacked∈Mq×q. We can now create SF2F∈Mn×nas a block-diagonal matrix of Sstackedmatrices. This describes the field-to-field effects, such as stage positioning errors when moving the lens from one field to the next.

The total estimate S=Savgf+Sstacked+SF2Fshould give a good estimate with a more limited amount of substrates p>>q, p>>r, where r is the number of inter-field parameters used.

The above model uses domain knowledge to reduce the effective degrees of freedom from which S can be estimated. The domain knowledge used is the assumption that the average field effect, inter-field effects and field-to-field effects cover most of the known contributors to overlay and that each have distinct root causes such that the cross-covariances between each of these subspaces is small. Depending on further knowledge about the presence or absence of these effects in the data, it may be possible to perform any one or two of the steps in the above model to obtain a useful estimate of S.

In general this approach can be generalized to the concept that domain knowledge gives us a mapping {right arrow over (s)}→S, where {right arrow over (s)} is a vector with a length significantly smaller than n2. With this mapping ŝ and therefore S can be found using a maximum likelihood estimate.

An alternative approach is to use some a priori statistical knowledge on the distribution of S. Instead of completely restricting S to some subspace, this would more gradually suppress unlikely outcomes of the estimates. One example of such an approach is the graphical lasso, which reduces the 1-norm of S; this is not base-independent, so domain knowledge is required to find a suitable base.

Alternatively, the covariance matrix could be determined without reliance on any customer measurements, for example based entirely on a priori knowledge of noise contributions in the machine.

As well as being used in the sampling scheme optimization embodiment described above, the covariance matrix (whether determined by one of the above approaches or otherwise) can also be used in a fitting process for estimating overlay error as set out in the embodiment below.

Having determined the covariance matrix, it is then applied to the design matrix. In order to estimate parameters based on a design matrix C without taking into account covariance the most likelihood estimate would be used: {right arrow over (p)}=(CT·C)−1·C·{right arrow over (x)}, where: {right arrow over (p)} is a vector containing the determined parameter values and {right arrow over (x)} are the overlay results for a substrate.

To include the covariance matrix, the maximum likelihood estimator of {right arrow over (p)} can be calculated as: {right arrow over (p)}=(CT·Σ−1·C)−1·C·Σ−1·{right arrow over (x)}.

It should be appreciated that while the above description is couched in terms of overlay and alignment, it is not so restricted. The methods disclosed herein can be used in metrology of any type of feature which can be measured/modeled (e.g., Critical Dimension, Focus, Side Wall Angle, etc.). The more expensive the metrology, the greater the added value of an intelligent measurement scheme of reduced size.

Further embodiments of the invention are disclosed in the list of numbered clauses below:

1. A method of determining a set of metrology point locations, said set comprising a subset of potential metrology point locations on a substrate; wherein said method comprises:

determining a relation between noise distributions associated with a plurality of said potential metrology point locations using existing knowledge; and using the determined relation and a model associated with said substrate to determine the set.

2. A method according to clause 1 wherein the relation describes the covariance between each of said plurality of potential metrology point locations.

3. A method according to clause 1 or 2, wherein the relation includes the relative magnitude of variances associated with said noise distributions.

4. A method according to clause 2, wherein the relation has the shape of a covariance matrix and the method further comprises, for each of the potential metrology point locations, minimizing the determinant of a matrix which is formed from: a design matrix describing said model with the potential metrology point location; and the covariance matrix.
5. A method according to any one of the preceding clauses, wherein the metrology point locations contained in the set are the metrology point locations which contribute the greatest level of informativity about the substrate for a pre-determined size of the set.
6. A method according to any one of the preceding clauses, further comprising the step of performing a metrology operation on said substrate using measurements obtained exclusively from metrology points located at said set of metrology point locations.
7. A method according to any one of clauses 1 to 5, further comprising the step of performing an alignment operation on said substrate using measurements obtained exclusively from metrology points located at said set of metrology point locations, during a lithographic process.
8. A method according to claim7, further comprising the step of performing a patterning operation on said substrate after performing said alignment operation.
9. A method according to any one of clauses 1 to 5, further comprising the step of modelling distortions in a lithographic process using measurements obtained exclusively from metrology points located at said set of metrology point locations.
10. A method of determining a model for fitting measurements, the model comprising a plurality of pre-determined base functions and coefficients associated with each of those base functions, the method comprising the steps of:
determining a relation between noise distributions associated with a plurality of measurement positions using existing knowledge; and
using the determined relation and calculated values of each base function at the measurement positions to determine the coefficients.
11. A method according to clause 10 wherein the relation describes the covariance between each of said plurality of measurement positions.
12. A method according to clause 10 or 11, wherein the relation includes the relative magnitude of variances associated with said noise distributions.
13. A method according to any one of clauses 10 to 12, wherein the step of determining the coefficients uses a least squares approach.
14. A method according to any one of clauses 10 to 12, wherein the step of determining the coefficients includes calculating a maximum likelihood estimator of a vector comprising the coefficients.
15. A method according to any one of the preceding clauses, wherein the existing knowledge includes previous measurements of noise data associated with the metrology point locations.
16. A method according to any one of the preceding clauses wherein the existing knowledge includes assumptions about the inter-relation between a plurality of factors which affect variation between the metrology point locations.
17. A method according to any one of the preceding clauses wherein, when determining the covariance measurement, the existing knowledge is used to reduce the degrees of freedom from which the covariance measurement can be determined.
18. A method according to any one of the preceding clauses, as applied in or to a lithographic process, wherein the relation is determined by calculating the average field per substrate from which measurement data is obtained.
19. A method according to any one of the preceding clauses, as applied in or to a lithographic process, wherein the relation is determined by calculating the inter-field effects between the fields on substrates from which measurement data is obtained.
20. A method according to any one of the preceding clauses, as applied in or to a lithographic process, wherein the relation is determined by calculating the field to field effects when moving between fields on the substrates from which measurement data is obtained.
21. A computer program comprising computer readable instructions which, when run on suitable computer apparatus, cause the computer apparatus to perform the method of any one of the preceding clauses.
22. A computer program product comprising the computer program of clause 21.
23. An apparatus having a processor specifically adapted to carry out the steps of the method as claused in any one of clauses 1 to 20.
24. An apparatus according to clause 23 which is specifically configured as a lithographic apparatus operable to perform a lithographic process on substrates.

The steps of the methods described above can be automated within the lithography apparatus control unit LACU shown inFIG. 1. This unit LACU may include a computer assembly as shown inFIG. 3. The computer assembly may be a dedicated computer in the form of a control unit in embodiments of the assembly according to the invention or, alternatively, be a central computer controlling the lithographic projection apparatus. The computer assembly may be arranged for loading a computer program product comprising computer executable code. This may enable the computer assembly, when the computer program product is downloaded, to control aforementioned uses of a lithographic apparatus with embodiments of the level and alignment sensors AS, LS.

Memory1229connected to processor1227may comprise a number of memory components like a hard disk1261, Read Only Memory (ROM)1262, Electrically Erasable Programmable Read Only Memory (EEPROM)1263and Random Access Memory (RAM)1264. Not all aforementioned memory components need to be present. Furthermore, it is not essential that aforementioned memory components are physically in close proximity to the processor1227or to each other. They may be located at a distance away

The processor1227may also be connected to some kind of user interface, for instance a keyboard1265or a mouse1266. A touch screen, track ball, speech converter or other interfaces that are known to persons skilled in the art may also be used.

The processor1227may be connected to a reading unit1267, which is arranged to read data, e.g. in the form of computer executable code, from and under some circumstances store data on a data carrier, like a floppy disc1268or a CDROM1269. Also DVD's or other data carriers known to persons skilled in the art may be used.

The processor1227may also be connected to a printer1270to print out output data on paper as well as to a display1271, for instance a monitor or LCD (Liquid Crystal Display), of any other type of display known to a person skilled in the art.

The processor1227may be connected to a communications network1272, for instance a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN) etc. by means of transmitters/receivers1273responsible for input/output (I/O). The processor1227may be arranged to communicate with other communication systems via the communications network1272. In an embodiment of the invention external computers (not shown), for instance personal computers of operators, can log into the processor1227via the communications network1272.

The processor1227may be implemented as an independent system or as a number of processing units that operate in parallel, wherein each processing unit is arranged to execute sub-tasks of a larger program. The processing units may also be divided in one or more main processing units with several sub-processing units. Some processing units of the processor1227may even be located a distance away of the other processing units and communicate via communications network1272. Connections between modules can be made wired or wireless.

The computer system can be any signal processing system with analogue and/or digital and/or software technology arranged to perform the functions discussed here.

The descriptions above are intended to be illustrative, not limiting. Thus, it will be apparent to one skilled in the art that modifications may be made to the invention as described without departing from the scope of the claims set out below. In addition, it should be appreciated that structural features or method steps shown or described in any one embodiment herein can be used in other embodiments as well.