Patent Abstract:
A system and methods for generating a variable rate filtered output using synchronous filters having the same filter sampling time, that avoids complexities of asynchronous filters. A system employs multiple filters that are staggered in time and set so that the output of one of the multiple filters is available whenever a secondary process requires state information. In another embodiment, the synchronous filters are programmable so as to change the filter sampling time. This configuration is possible when it is known that the prescribed time interval of the secondary process is longer than the filter sampling time and the prescribed time interval is an integer multiple of the filter sampling time. By using programmable filters, the number of filters required to accommodate a certain prescribed time interval can be minimized. Whether a system employs multiple non-programmable filters staggered in time or programmable filters, the invention achieves producing a variable rate filtered output while using synchronous filters. Thus, computationally intensive and complicated variable rate filters can be avoided while minimizing processor requirements. In another aspect of the invention, the inventive method of generating a variable rate filtered output is employed in a lithography system to control the movements of various components therein. In one embodiment, the filtered outputs are used to estimate the relative positions of the reticle and the wafer. With the variable rate feature, the lithography system can dynamically accommodate for the noise arising from component movements and other sources in the wafer exposure process.

Full Description:
FIELD OF THE INVENTION 
   The invention relates to the field of filters, and more particularly to digital filters that can produce an output at a variable rate. 
   BACKGROUND OF THE INVENTION 
   Asynchronous digital filters are used in many applications, including navigation, process control and machine calibration. Asynchronous filters provide a filtered output of an input signal at a variable rate, hence the asynchronous appellation (i.e., filter output is available on demand). In contrast, synchronous filters provide a filtered output at a constant rate. In the area of position control, for example, the input signal to the filter may be a sensor sample that is a measurement of velocity, position, rotational rate or angle of a spinning vehicle or body. The filter output gives an indication of the orientation or trajectory of the vehicle or body. The orientation or the trajectory of a body is often described by a fixed number of variables defined as the state of the system being observed. 
   Typically, an asynchronous filter receives at least one sensor sample during its time period and produces a filtered output for use by a secondary process or system. The secondary process or system may be a control system that uses the state of the system, as indicated by the filter output, to either change the state of the system or to compensate for perturbations in the state. For example, the control system of a missile or a robot arm will correct for inaccuracies between the desired trajectory and the actual trajectory as indicated by the filter output. In other applications, the filter may give an indication of other system states and the control system may perform other functions. 
   Filtering is necessary because measurement of physical phenomena (e.g., position, velocity, acceleration and pressure) usually results in a signal that is corrupted by noise and other sources of deviations from the actual measurement desired. The secondary process typically cannot meaningfully use noisy sensor samples. However, if the statistical properties of the noise are known, a filter can be designed to produce a more accurate estimate of the system states based on the noise-corrupted state measurements. In addition to filtering the measured signal, certain corrections and transformations of the state estimates, such as coordinate transformations, may also be made by the filter. 
   The variable rate feature of asynchronous filters is highly desirable because in many applications the rate at which the secondary system desires the filter output may not be constant. Furthermore, the window during which the secondary process may accept information from the filter may be restricted. As the process changes, the secondary system, which may need to make determinations about the state of the system or adjustments more often, may require more frequent and accurate updates from the filter. If a filter generates outputs at a fixed rate, the secondary system may make determinations or adjustments that are based on older and less accurate information. Consequently, a variable rate or asynchronous filter is highly desirable because it provides a filter output based on more recent sensor samples and provides a filter output that can be used by the secondary system at the time the secondary system needs the filter output, thus reducing data age. To further reduce the effects of data age, prediction may be used to extend the filtered signal into the future. Providing a filter output at or close to the time that the secondary system needs the filter output shortens the prediction time, thereby reducing the errors. 
   Asynchronous digital Kalman filters are one type of asynchronous digital filter. More details on asynchronous Kalman filters can be found in  Applied Optimal Estimation  by Arthur Gelb, MIT Press, Cambridge, Mass., 1974. Kalman filters have many desirable properties including optimality, recursive solutions, and ease of digital implementation. Unfortunately, asynchronous Kalman filters are typically very complicated and, consequently, computationally intensive and slow. The intensive computational requirements of an asynchronous Kalman filter will limit the maximum sampling frequency. As a result, the asynchronous filter may be unable to produce an output at a rate necessary for the secondary process. 
   Synchronous Kalman filters are simpler to design and less computationally intensive, hence they are faster. More details on synchronous Kalman filters can be found in  Adaptive Filtering: Prediction and Control  by Graham C. Goodwin and Kwai Sang Sin, Prentice-Hall, Inc., Englewood Cliffs, N.J. Unfortunately, a single synchronous Kalman filter is not well suited to applications that require filtered output at a variable rate. Consequently, there is a need for synchronous filters, whether of a Kalman or another type, to produce filtered output at a variable rate. The invention meets this need. 
   In some lithography systems, radiation from a source exposes specific areas of a wafer, that is coated with a resist sensitive to the radiation, as the wafer travels relative to the source. In this manner, a desired pattern is exposed in the resist on the wafer. The exactness of the movement of the wafer beneath the radiation source is therefore critical. It is desirable to have a control system that obtains exact state updates of the wafer as it travels relative to the radiation source. As information about the path traversed by the wafer is sampled, the signal including the measurement samples includes noise and other sources of deviations. Since statistical properties of some noise and deviations are known, filters are used to produce a more accurate estimate of the actual position based on the noisy measurement sample. In essence, the filter acts to “remove” such characterized noises and deviations. The invention provides an approximately asynchronous filter for more accurately correcting the errors associated with the motion of various components in lithography systems. Such an asynchronous filter is essential in a system where the corrections are made by a subsystem that can only accept data during a small window of time, and this window does not occur at a fixed rate. The filter, in addition to signal processing, may also use prediction to compensate for delays, and may also perform deterministic corrections and transformations on the sensor data and/or the state estimates. 
   SUMMARY OF THE INVENTION 
   A system and methods that provide a simple effective technique for generating variable rate filtered output while using synchronous filters having the same fixed filter update time are described. The system employs multiple filters having outputs are staggered in time. The amount by which each filter is staggered in time is set so that the output of one of the multiple filters is available whenever a secondary process requires state information. 
   In another embodiment, the synchronous filters are programmable so as to change the filter update time. This configuration is possible when it is known that the frequency at which the secondary process requires data from the filter (the input frequency) is smaller than the filter update frequency and the filter update frequency is an integer multiple of the input frequency. By using programmable filters, the number of filters required to accommodate the data requirements of the secondary process can be minimized. 
   Whether a system employs multiple non-programmable filters staggered in time or programmable filters, the invention achieves a variable rate filtered output while using synchronous filters. Thus, computationally intensive and complicated variable rate filters can be avoided, while at the same time minimizing processor requirements. 
   In another aspect of the invention, a method of generating a variable rate filtered output is employed in a lithography system to control the movements of various components therein. In one embodiment, the filtered outputs are used to estimate the position of the wafer stage. With the variable rate feature, the lithography system, in which the correction subsystem can only accept stage state information within restricted windows in time and the rate at which these windows occur is not constant, can dynamically correct for the position errors in the stage during the wafer exposure process. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a block diagram of a lithography system according to the presently preferred embodiment of the invention; 
       FIG. 2  shows a detailed diagram of a filter and a predictor in accordance with the embodiment of  FIG. 2 ; 
       FIG. 3  is a timing diagram of the operation of a synchronous filter architecture according to the embodiment of  FIG. 2 ; 
       FIG. 4  is a timing diagram of the operation of a synchronous filter architecture according to the embodiment of  FIG. 2 ; 
       FIG. 5  is a timing diagram of the operation of a synchronous filter architecture according to the embodiment of  FIG. 2 ; 
       FIG. 6  is a block diagram of a filter architecture according to an alternate embodiment of the invention; 
       FIG. 7  is a timing diagram of the operation of a filter architecture according to the embodiment of  FIG. 6 ; 
       FIG. 8  is a timing diagram of the operation of a filter architecture according to the embodiment of  FIG. 6 ; 
       FIG. 9  is a timing diagram of the operation of a filter architecture according to the embodiment of  FIG. 6 ; and 
       FIG. 10  is a timing diagram of the operation of a filter architecture having multiple programmable filters. 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     FIG. 1  shows a system block diagram of an electron beam lithography system (System)  200  according to the presently preferred embodiment of the invention. System  200  includes a moving stage  220  that carries a resist-coated wafer  222  for exposure by an electron optical column  214 . System  200  also includes a sensor  204  that measures the position of the wafer stage  220 . The sensor  204  is coupled to a filter  206 . The filter  206  is connected to a predictor  208  that in turn is connected to a controller  212 . The controller  212  is connected to the electron optical column  214 . The filter  206  and the predictor  208  are shown as separate units in  FIG. 1 ; however, they may also be implemented together as a single unit. A controller (not shown) uses the output of the sensor  204  to control the position of the stage  220 . 
   The filter  206  in the preferred embodiment of the invention is a Kalman filter; however, other filters may be used, such as a lattice filter or a finite impulse response (FIR) filter. In the preferred embodiment, the Kalman filter is implemented using a dedicated digital signal processor (DSP) chip. Although the filter is described as using a dedicated DSP chip, those skilled in the art understand that the invention may be implemented using hardware exclusively or a mixture of software and hardware. Also, the filter, in addition to signal conditioning, may carry out other computations such as coordinate transformation and/or deterministic correction of the state measurements and/or state estimates. 
   The electron optical column  214  includes an electron source, apertures, and magnetic or electrostatic lenses and deflectors. These elements produce an electron beam at a specified location on the wafer with a well-defined shape and focused in the plane of the wafer. The electron optical column&#39;s deflectors control the location of the electron beam at the wafer. The electron optical column also includes a blanking unit that effectively turns the electron beam on or off at the wafer, so that exposure of the wafer by the beam can be controlled. Using the blanker and the deflectors, a pattern can be exposed in the resist coating the wafer surface. After developing the exposed resist, further semiconductor processing operations can be performed on the selectively exposed wafer surface. The electron optical deflectors can only deflect the electron beam over a small fraction of the wafer surface. In order to cover the rest of the surface, the wafer stage must move the wafer in coordination with the electron beam deflectors. The controller  212  controls the deflectors. The invention is preferably directed to a type of electron beam lithography system where the stage moves while the pattern is being written. In order to accurately place the deflected beam on the wafer  222 , the controller  212  must have up-to-date information about the wafer stage  220  location. Prior to the start of the wafer exposure procedure, the wafer  222  is rigidly attached to the wafer stage assembly  220  by means of mechanical clamps or an electrostatic chuck. An alignment procedure is then employed to locate previously patterned features on the wafer relative to the wafer stage coordinates and to the electron beam. From that point on, location of the wafer stage assembly  220  can be directly related to the location of features on the wafer  222 . Thus, the new pattern being exposed on the wafer can be accurately aligned with respect to structures on the wafer created by earlier lithography operations. 
   The signals generated by the controller  212  to control the deflectors are analog. However, the processed positional information from the predictor  208  is digital, as is the pattern information sent to the controller by a main computer where the wafer pattern information is stored. Thus, digital-to-analog conversion is required within the controller  212 . The analog signals going to the deflectors must be completely free of any noise, as such noise would perturb the pattern placement on the wafer. Therefore, while the electron beam is un-blanked, no digital information can be received by or processed by the controller  212 . Digital information can be received by or processed by the controller  212  only when the beam is blanked. Furthermore, the length of time when the beam is un-blanked may vary, depending on the exposure conditions required for the pattern. These conditions require that the filter  206  and the predictor  208  operate in an asynchronous mode. However, if all operations of the system, including the exposure process, can occur in coincidence with transitions of a master clock, or offset in time by a fixed amount from transitions of a master clock, then the filter  206  and the predictor  208  can operate pseudo-asynchronously. 
   The controller  212  requires up-to-date information about the position of the wafer stage assembly  220  because the precise path of the stage cannot always be known a priori with sufficient accuracy. Ideally, the path traversed by the stage coincides with the path required to place the wafer in the proper location for patterning. However, due to vibrations and inaccuracies in the stage control system, motors and drivers (not shown) of the wafer stage assembly  220 , the wafer stage path may deviate from the ideal path, so that the wafer is not in the expected location when the new pattern is being exposed. 
   This misalignment is unavoidable in normal operation. It can be compensated for by deflecting the electron beam appropriately during the pattern exposure. The amount of deflection required will depend on the relative position error of the wafer stage assembly  220 . 
   The deflection of the electron beam is determined by the controller  212 , which provides an analog signal to amplifiers that drive the deflectors within the electron optical column  214 . To effect this deflection, the controller  212  is provided with the position error estimates of the wafer stage assembly  220 . The position error estimate is generated from a position measurement made by the sensor  204  that detects the position of the wafer stage assembly  220 . The sensor  204  may be a heterodyne interferometer system that measures the distance of plane mirrors affixed to the wafer stage assembly from interferometer heads that are fixed in position relative to the electron optical column  214 . Such systems are sold commercially by, for example, Hewlett-Packard, and are well known in the art. The output of sensor  204  in that case is a digital signal representing the position of the wafer stage assembly  220 . These position measurements are compared to the expected stage position from the stage controller to yield position errors. They are used by the filter  206  to generate relative position error estimates of the wafer stage assembly  220 . In a similar manner, stage velocity and acceleration are derived from the position measurements by numerical differentiation, and velocity and acceleration error estimates are determined. 
   Typically, the signal produced by the sensor  204  includes noise and other sources of deviations (such as quantization). The controller  212  cannot meaningfully use noisy sensor samples. However, if the statistical properties of the noise are known, the filter  206  can be designed to produce a position error estimate of the actual position (or velocity, or acceleration) of the wafer stage assembly  220  using the noisy measurements of the sensor  204 . The position error estimate of the filter  206  is likely to be closer to the actual position than that measured by the sensor  204 . 
   Since there typically is a time delay between acquisition of the position information from the sensor  204  and output of the corresponding processed position error estimate by the filter  206 , as well as a time lag in sending the filtered signal to the controller  212  and commanding the deflectors to produce the required deflection, a predictor  208  is coupled to the filter  206 . Without the predictor  208 , the position error estimate from the filter  206  is likely to be inaccurate or “stale” by the time the position error estimate passes from the filter to the controller  212  and on to the deflectors in the form of deflection signals. The predictor  208  supplies information needed by the controller  212  to allow it to provide the correct positional error at the time the deflectors will be deflecting the un-blanked beam. 
   The predictor  208  takes the filtered errors in position, velocity and acceleration from the filter  206 , and generates the future state of the system as follows. If the stage position, velocity and acceleration errors in the directions x and y at the filter  206  update time t o  are defined as x 0 , y 0 , vx 0 , vy 0 , ax 0  and ay 0 , respectively, then the predicted x and y position errors of the stage at a later time t are approximated by the following expressions: 
     x   p   =x   0   +vx   0 ( t−t   0 )+ ax   0 ( t−t   0 ) 2 /2
 
 y   p   =y   0   +vy   0 ( t−t   0 )+ ay   0 ( t−t   0 ) 2 /2
 
   Furthermore, the predicted stage velocity errors are given by the following expressions:
 
 vx   p   =vx   0   +ax   0 ( t−t   0 )
 
 vy   p   =vy   0   +ay   0 ( t−t   0 ).
 
   The acceleration errors are approximated as constant: ax p =ax 0 ; ay p =ay 0 . 
   In this example, the stage assembly  220  is assumed to be travelling rectilinearly in the x-y plane. More generally, regarding the stage as a rigid body, the stage position and orientation will require six degrees of freedom to fully define the location of a point on the wafer  222 . 
   After information from the predictor is loaded into the controller  212 , it is converted into analog signals that go to the deflector amplifiers. The time between the analog conversion and the filter  206  update time, i.e., t−t 0 , is substituted into the above equations to provide the quantities x p , y p , vx p , vy p , ax p  and ay p  that are needed by the controller  212 . As mentioned above, these quantities must be loaded while the beam is blanked and no exposure is occurring, so that digital noise associated with the data transfer and attendant operations does not perturb the analog deflection signals. 
   During the exposure, the stage moves a non-negligible distance, requiring the deflectors to be continually updated. Thus, a calculation equivalent to the above must be carried out continuously within the controller  212  using analog techniques and involving the information from the predictor  208 . 
   Since the stage acceleration is generally not constant in time, the above position prediction from the predictor  208  will be valid (i.e., within acceptable accuracy) for only a limited amount of time. Then new values of the quantities x 0 , y 0 , vx 0 , vy 0 , ax 0  and ay 0  must be determined from more recent position information from the filter  206 . This assumes that the time over which the quantities x 0 , y 0 , vx 0 , vy 0 , ax 0  and ay 0  are valid is greater than the time between the filter  206  update time and the conversion of the predicted quantities to analog form in the controller  212 . Furthermore, the total beam exposure time may exceed the time during which the quantities x 0 , y 0 , vx 0 , vy 0 , ax 0  and ay 0  are valid. In that case, the beam must be blanked, interrupting the exposure, and current values of the quantities x 0 , y 0 , vx 0 , vy 0 , ax 0  and ay 0  are used to calculate x p , y p , vx p , vy p , ax p  and ay p , which are then loaded into the controller  212 . The exposure can then be continued after the controller  212  has updated the deflector conditions. 
     FIG. 2  shows a diagram of the filter  206  and the predictor  208  in accordance with the invention. The filter  206  and the predictor  208  can be used to generate position estimates at various rates. In  FIG. 2 , the filter  206  and the predictor  208  are shown as filter-predictors  206 - 8 - 1 ,  206 - 8 - 2 ,  206 - 8 - 3 ,  206 - 8 - 4 , each of which performs both filtering and prediction. While in  FIG. 2  each filter is shown with a matching predictor, an alternate embodiment may have several filters and only one predictor, provided that the predictor algorithm is fast enough. Each of the filter-predictors  206 - 8 - 1 ,  206 - 8 - 2 ,  206 - 8 - 3 ,  206 - 8 - 4  periodically accepts a position measurement sample of the signal generated by the sensor  204 . For ease of reference, each filter-predictor combination will be simply described as a filter and will refer to the filter-predictor combination unless explicitly indicated otherwise. 
   Each of the filters  206 - 8 - 1 ,  206 - 8 - 2 ,  206 - 8 - 3 ,  206 - 8 - 4  produces a periodic filtered output using the state measurement samples periodically accepted by each filter. The filters were sequentially initialized, so that their outputs occur during sequential clock pulses. The periodic filtered output produced by each of the filters  206 - 8 - 1 ,  206 - 8 - 2 ,  206 - 8 - 3 ,  206 - 8 - 4  is applied to a multiplexer  206 - 4 . A control logic  206 - 6  and a clock  206 - 7 , which is a system master clock or is synchronized with the system master clock, are each independently coupled to the multiplexer  206 - 4 . The control logic  206 - 6  generates control signals that are applied to the multiplexer  206 - 4 . At the beginning (or end) of each clock cycle generated by the clock  206 - 7 , or at a fixed time offset, depending on the control signals generated by the logic  206 - 6 , the multiplexer  206 - 4  selects one or none of the periodic filtered outputs applied at the inputs to the multiplexer  206 - 4 . If the multiplexer  206 - 4  selects one of the periodic filtered outputs applied at the inputs to the multiplexer  206 - 4 , the multiplexer  206 - 4  transfers the selected periodic filtered output to the output of the multiplexer  206 - 4 . Over a period of several clock cycles, the output of the multiplexer  206 - 4  produces an output sequence of state estimates (or filtered state measurement samples) using the periodic filtered outputs accepted by the multiplexer. By manipulating the control signals, the control logic  206 - 6  can create multiplexer  206 - 4  output sequences with a variety of frequencies. 
   Each state estimate produced at the output of the multiplexer  206 - 4  is stored in a data register  206 - 5  until it is replaced by the next state estimate produced at the output of the multiplexer  206 - 4 . The state estimate can then be transferred to the controller  212  at an appropriate time while the beam is blanked. From the above information (i.e., x p , y p , vx p , vy p , ax p , ay p , t−t p ), the controller  212  can establish the necessary analog signal corrections to the deflectors. 
   Generation of state estimates at various rates using the embodiment shown in  FIG. 2  is readily apparent from the timing diagrams depicted in  FIGS. 3 ,  4  and  5 . In  FIGS. 3 ,  4 , and  5 , the exposure cycle (or input sequence) indicates the frequency and relative timing at which the controller  212  (or secondary process) requires state estimates. In  FIG. 3 , the exposure cycle has a prescribed time interval T be  that is five clock cycles long and includes a blanking period followed by an exposure period. T f  is defined as the filter update time. After the state error predictions are received by the controller  212 , the deflector digital-to-analog convertors (DACs) are loaded prior to the analog deflector signal generation. In  FIG. 3 , the controller  212  requires state estimates every five clock cycles as indicated by the exposure cycles  301 - 308  that make up the input sequence and that repeat every five clock cycles. In  FIG. 4 , the controller  212  requires state estimates every three clock cycles. In  FIG. 5 , the controller  212  requires state estimates every six clock cycles. In each situation depicted by  FIGS. 3 ,  4  and  5 , the embodiment shown in  FIG. 2  produces the state estimates as needed by the controller  212  even though each of the filters  206 - 8 - 1 ,  206 - 8 - 2 ,  206 - 8 - 3 ,  206 - 8 - 4  produces a state estimate every filter update time of four cycles. More specifically, in  FIGS. 3 ,  4 , and  5 , during each input window, or blanking period, of the exposure cycle, there is a state estimate produced by one of the filters  206 - 8 - 1 ,  206 - 8 - 2 ,  206 - 8 - 3 ,  206 - 8 - 4 . The multiplexer  206 - 4 , pursuant control signals generated by logic  206 - 6 , selects the state estimate that is coincident with or aligned with the current input window. Alternatively, instead of describing the multiplexer  206 - 4  as selecting a state estimate, the multiplexer  206 - 4  can be described as selecting a certain filter for each of the input windows in the input sequence. The control logic  206 - 7  provides the control signals that cause the multiplexer  206 - 4  to select from among the state estimates, resulting in the coincidence of the selected state estimates and the input windows for the controller  212 . It should be clear that the coincidence, for the purposes discussed herein, may be satisfied when a state estimate is coincident with an input window or when a state estimate is a state estimate that is produced before the start (or rise) time of an input window and that is not so old (stale) or inaccurate as to be useless to the controller  212 . 
   In each of  FIGS. 3 ,  4  and  5 , at the same time that a state estimate is produced by a filter, a state measurement sample, that will be used to produce the next state estimate one filter update time later, is accepted by the same filter. However, a given filter is not limited to only this measurement and may use successive as well as earlier measurements in order to calculate its next output. In  FIGS. 3 ,  4  and  5 , each of the filters accepts a state measurement sample every four cycles of the master clock, but may not be limited to this one measurement. Filter  206 - 8 - 1  accepts its state measurement sample: one clock cycle before filter  206 - 8 - 2  accepts its state measurement sample; two clock cycles before filter  206 - 8 - 3  accepts its state measurement sample; and three clock cycles before filter  206 - 8 - 4  accepts its state measurement sample. In summary, there is one clock cycle between the time any one of the filters accepts its state measurement sample and the nearest time another filter accepts its state measurement sample. It should be apparent that in other representative implementations of this embodiment, the number of clock cycles between the time any one of the filters accepts its state measurement sample and the nearest time at which another filter accepts its state measurement sample may be different. Also, this assumes that the rate at which the sensors can be sampled is fast enough (in the present example, one sample per clock cycle). The above statements also apply to the production of state estimates by the filters. 
   By using several computationally non-intensive and relatively simple filters, it is possible to provide state estimates over a range of allowable frequencies. Consequently, it becomes possible to avoid variable rate filters that are processor intensive as well as complex to develop. 
   The state estimates are produced with the same filter update time of four cycles at each of the filters  206 - 8 - 1 ,  206 - 8 - 2 ,  206 - 8 - 3 ,  206 - 8 - 4 . However, the filters  206 - 8 - 1 ,  206 - 8 - 2 ,  206 - 8 - 3 ,  206 - 8 - 4  do not produce the state estimates at the same time. Rather, each filter produces its state estimate at a fixed time period offset from the time of production by another filter. In  FIG. 3 , for the first input window, the first state estimate generated by filter  206 - 8 - 1  is selected by the multiplexer  206 - 4  to apply to the data register  206 - 5 . For the second input window, the second state estimate generated by filter  206 - 8 - 2  is selected by multiplexer  206 - 4 . For the third input window, the third state estimate generated by filter  206 - 8 - 3  is selected by multiplexer  206 - 4 . For the fourth input window, the fourth state estimate produced by filter  206 - 8 - 4  is selected by multiplexer  206 - 5 . It should be apparent that for the fifth, sixth, seventh and eighth windows, the state estimates from the filters  206 - 8 - 1 ,  206 - 8 - 2 ,  206 - 8 - 3  and  206 - 8 - 4 , respectively, are selected by the multiplexer  206 - 4 . So long as T be  equals five cycles and T f , the filter update time, equals four cycles, the multiplexer  206 - 4  sequentially selects state estimates from the filters in the following repetitive order: filter  206 - 8 - 1 , filter  206 - 8 - 2 , filter  206 - 8 - 3  and filter  206 - 8 - 4 . At each of the windows shown in  FIG. 3 , a filter will do at least the following two things: 1) read state information from the sensor to be used in generating a state estimate a time T f  later; and 2) make a state estimate available using state measurement information received from the sensor at earlier times. 
   In  FIG. 4 , the exposure cycle time has been changed to three cycles whereas T f  still equals four cycles. The multiplexer  206 - 4  sequentially selects state estimates from the filters in the following repetitive order: filter  206 - 8 - 1 , filter  206 - 8 - 4 , filter  206 - 8 - 3  and filter  206 - 8 - 2 . 
   In  FIG. 5 , the exposure cycle time has been changed to six cycles whereas T f  still equals four cycles. The multiplexer  206 - 4  sequentially selects state estimates only from filters  206 - 8 - 1  and  206 - 8 - 3  in the following repetitive order: filter  206 - 8 - 1  followed by filter  206 - 8 - 3 . In this case, only two filters are required; filters  206 - 8 - 2  and  206 - 8 - 4  are never selected. 
   System  200  has been described in connection with four filters. However, there can be more or fewer filters depending on the allowable length of the exposure cycle time as well as the minimum length of the filter update time. The number of filters can be calculated using the following equation:
 
Number of filters=maximum of {( T   f   /T   be )* n , for all allowable  T   be },
         where n is the smallest integer that will result in an integer value for (T f /T be )*n, T f  is the filter update time and T be  is the period of the secondary process (i.e., the period of the process that requires the filter output).       

     FIG. 6  shows an illustrative system block diagram of an alternative lithography system (System)  600  according to the presently preferred embodiment of the invention. The system  600  includes a moving wafer stage assembly  620  and an electron optical column  614  that projects electrons onto a wafer  622  placed on the wafer stage assembly  620 . The system  600  also includes a sensor  604  that detects the position of a reticle stage assembly  602 . The sensor  604  is coupled to a programmable filter  606  through a sampling circuit (not shown) and possibly an analog-to-digital converter (not shown). The programmable filter  606  is coupled to a predictor  608  that in turn is coupled to a controller  612  that in turn is coupled to the electron optical column  614 . Instead of having many fixed filter update time filters from which filtered samples may be selected in order to provide state estimates, the filter update time of programmable filter  606  can be changed such that the filter  606  produces a state estimate at least as often as the controller  612  requires an estimate. 
   The programmable filter  606  can be used where it is known a priori that all allowable values of the exposure cycle time T be  of the secondary process (or the controller  612 ) will always be longer than the filter update time of the filter  606 . More specifically, if the exposure cycle time is known to be fixed over a certain time period and it is known when it will change and to what value it will change, a programmable filter as in the system  600  can be used so long as, for each value of the exposure cycle time, the exposure cycle is an integer multiple of the programmable filter update time. For a filter that accepts state measurement samples with the same frequency as the filter update frequency, a short filter update time ensures predictions based on more recent measurements than would be the case for a filter with a longer update time. Thus, it is usually desirable to make the filter update time an integer sub-multiple of the exposure cycle time. Other types of filters that accept state measurement samples more frequently may function effectively with a filter update time equal to the exposure cycle time. 
   In the present embodiment of the invention, the exposure cycle time includes a blanking time and an exposure time. The blanking time T b  is the input window during which the secondary process (or the controller  612 ) can accept a state estimate. The exposure time is the time during which the controller  612  cannot accept a state estimate and is equal to T be −T b . The exposure time is determined by the electron beam intensity and the wafer resist properties. For some values of the exposure cycle time, it may be necessary to change the blanking time so that the value of the exposure cycle time after adjusting the blanking time is an integer multiple of the filter update time. This will usually be the case when T be  is equal to the clock cycle multiplied by a prime number. For some values of the exposure cycle time, it may be necessary to change the filter update time to a value larger than the minimum filter update time, so that the exposure cycle time is an integer multiple of the filter update time. Table 1.1 below shows how the filter update time (or blanking time, or both) are adjusted for various operating conditions. T fm  is the minimum filter update time and depends on the available computational resources and the complexity of the filter algorithm. 
   
     
       
             
             
             
             
             
           
         
             
                 
               TABLE 1.1 
             
             
                 
                 
             
             
                 
               T fm  (min.) 
               T be  (before) 
               T f  (after) 
               T be  (after) 
             
             
                 
                 
             
           
           
             
                 
               5 cycles 
                6 cycles 
               6 cycles 
                6 cycles 
             
             
                 
               5 cycles 
               14 cycles 
               7 cycles 
               14 cycles 
             
             
                 
               5 cycles 
               18 cycles 
               6 cycles 
               18 cycles 
             
             
                 
               5 cycles 
               19 cycles 
               5 cycles 
               20 cycles 
             
             
                 
               5 cycles 
               23 cycles 
               6 cycles 
               24 cycles 
             
             
                 
                 
             
           
        
       
     
   
   A representative depiction of the operation of the system  600  is shown in the timing diagrams of  FIGS. 7   a ,  7   b ,  8   a ,  8   b ,  9   a  and  9   b . In  FIG. 7   a , the prescribed time interval or exposure cycle time is six cycles long. The programmable filter  606  initially has a filter update time of five cycles. The filter  606  can be reprogrammed to produce state estimates every six cycles as depicted in  FIG. 7   b . Reprogramming the filter  606  includes changing the parameters of the Kalman filter to account for the changed update time. Reprogramming of Kalman filters is well known in the art. Timing diagrams representative of another operating situation are shown in  FIGS. 8   a  and  8   b , where the exposure cycle time is fourteen cycles. To produce a state estimate when the secondary process can accept it, the filter  606  is reprogrammed such that the filter update time is increased from five cycles to seven cycles. 
   Timing diagrams representative of another operating situation are shown in  FIGS. 9   a  and  9   b , where the exposure cycle time is seventeen cycles long. Since the number of cycles in the exposure cycle time is a prime number, changing the filter update time by itself is not sufficient to make the exposure cycle time an integer multiple of the filter update time. Consequently, the blanking time T b  is increased as illustrated in  FIG. 9   b . As a result, the exposure cycle time is now eighteen cycles. Concomitantly, the filter update time has been increased by one cycle to six cycles so that the exposure cycle time is an integer multiple of the filter update period. 
     FIG. 10  illustrates the timing diagram for a system similar to the system  200 , but which has programmable filters instead of filters with a fixed filter update time. Instead of having many filters with a fixed filter update time from which filtered samples may be selected in order to provide state estimates, a programmable filter permits changing the filter update time to match the needs of a secondary process for a state estimate. For an exposure cycle time of nine cycles, and a T f  of five cycles, five non-programmable filters are needed to produce a state estimate whenever the secondary process can accept one. The five filters can be reduced to two filters if the two filters can be programmed so as to have a filter update time of six cycles. 
   Alternatively, a single programmable filter having a filter update time of five cycles can be used, if the exposure cycle time is increased to ten cycles. However this requires increasing the total blanking time, as is also the case in  FIG. 9   b . Since an increase in blanking time reduces the system throughput, this approach should be avoided if possible. 
   As mentioned earlier for the first embodiment, the total exposure time may exceed the period over which the predictions from the filter-predictor are valid. In that case, extra blanking periods may be inserted during the exposure time, so that predictor updates can be made without disturbing the analog output of the deflector drivers. Thus, these extra blanking periods should coincide with updates from the filter-predictor(s). If possible, the filter update time should be adjusted to be an integer sub-multiple of the new total exposure time, using the methods described above. If the new total exposure time, after inclusion of extra blanking periods, is T be ′, the following relationship should preferably be satisfied: N u T f =T be ′=T be +(N u −1)T b , where N u  is the number of update times during the new exposure cycle. In order to maximize the system throughput, the total blanking time should be as small as possible. Thus, T f  should be as long as possible, consistent with the required prediction accuracy, and N u  should be as small as possible. 
   If it is not possible to find integers satisfying this relationship, a longer blanking time may be added for the last filter-predictor update so that T be ′ becomes an integer multiple of T f . That is, if N u  is the largest integer satisfying the relationship N u T f &lt;T be +(N u −1)T b , a final blanking period T b ′ of duration T b ′=(N u +1) T f −T be −(N u −1)T b  will provide an exposure cycle T be ′ equal to T f  multiplied by the integer N u +1. 
   The benefits of using a programmable filter include flexibility and reduction in the number of filters required in an application. Where possible, it may be preferable to have only one DSP chip running one filter instead of a staggered filter structure where several DSP chips are running several filters staggered in time. 
   Although the invention has been described in conjunction with particular embodiments, it will be appreciated that various modifications and alterations may be made by those skilled in the art without departing from the spirit and scope of the invention. The invention is not to be limited by the foregoing illustrative details, but rather is to be defined by the appended claims.

Technology Classification (CPC): 7