I claim:
1. A control system having input means and output means, for contolling a controlled system having states which are represented by a state vector, comprising means for applying said state vector to said input means of said control system, said control system providing, at said output means, correcting variables which are represented by a correcting variables vector, the relation between said state vector and said correcting variables vector being defined by a matrix of weights, and further comprising equation solving means for algorithmically solving an optimization equation in real time and providing a solution of said optimization equation, and means for applying said state vector to said equation solving means, said solution of said optimization equation being transferred to said control system to determine said weights of said matrix, whereby said control system provides an optimal correcting variables vector.
2. A control system as claimed in claim 1, wherein said optimization equation is the state dependent Riccati equation.
3. A control system as claimed in claim 1 and further comprising: adaptive model means for modeling said controlled system and for providing, from said state vector and said correcting variables vector, an estimated value of said state vector, means for applying said state vector and said correcting variables vector to said adaptive model means, means for forming the difference of said state vector and said estimated value of the state vector to provide a first vectorial training signal for training said adaptive model means, and means for training said adaptive model means to reduce said difference, thereby to provide a trained model, means for deriving a second vectorial training signal from said trained model of said controlled system, a control system-side network, and means for training said control system-side network in accordance with said second vectorial training signal, means for applying said state vector and said correcting variables vector to said control system-side network, said network being trained by said second vectorial training signal to provide therefrom a correction value to be applied to said optimal correcting variables vector to form an actual correcting variables vector applied to said controlled system.
4. A control system as claimed in claim 3, wherein said adaptive model of said controlled system comprises first matrix means representing a first matrix and second matrix means for representing a second matrix, means for multiplying said state vector by said first matrix, and means for multiplying said second matrix by said correcting variables vector, means for summing said state and correcting variables vectors each multiplied by the respective one of said matrices to provide an estimated model value of the time derivative of said state vector, and means for integrating said time derivative to provide an estimated model value of said state vector, and further comprising controlled system-side adaptive network means having an input and an output for generating at said output a correction value to be applied to said estimated model value of said state vector, and means for training said controlled system-side adaptive network means in accordance with said first vectorial training signal.
5. A control system as claimed in claim 3, comprising means for applying said first vectorial training signal to said output of said controlled system-side adaptive means to provide, at said input thereof, said second vectorial training signal by backpropagation.
6. A control system as claimed in claim 1, and further comprising: an adaptive structure trained off-line and having an input and an output, said state vector being applied to said input and said adaptive structure providing said correcting variables vector at said output, said correcting variables of said correcting variables vector being applied to said controlled system; said state vector being applied on-line to said equation solving means, whereby said equation solving means provide an optimal correcting variables vector, means for forming the difference of said correcting variables vector provided by said adaptive structure and said optimal correcting variables vector, and means for training said adaptive structure on-line in accordance with said difference.
7. A control system as claimed in claim 1, wherein said equation solving means comprise: a matrix structure having variable elements and copying said optimization equation, said matrix structure having matrix elements and linking an input and an output, said state vector being applied to said input, and means for varying said matrix elements in real time depending on an output vector at said output to make said vector 0, and means for outputting said matrix elements as said solution of said optimization equation.
8. A control system as claimed in claim 7, wherein said matrix structure is an adaptive structure, and further comprising means for training said adaptive structure with a training signal represented by the deviation of said output vector from 0.
9. A control system as claimed in claim 7, comprising a parallel processor for vector-matrix multiplication having parameters, said processor defining said matrix structure, and control loop means for varying said parameters depending on the deviation of said output vector from 0.
9. A control system having input means and output means, for contolling a controlled system having at least one state represented by a state vector, comprising means for applying said state vector to said input means of said control system, said control system providing, at said output means, at least one correcting variable represented by a correcting variables vector, the relation between said state vector and said correcting variables vector being defined by a factor or a matrix of weights; and further comprising: adaptive model means for modeling said controlled system and for providing, from said state vector and said correcting variables vector, an estimated value of said state vector, means for applying said state vector and said correcting variables vector to said adaptive model means, means for forming the difference of said state vector and said estimated value of the state vector to provide a first vectorial training signal for training said adaptive model means, and means for training said adaptive model means to reduce said difference, thereby to provide a trained model, means for deriving a second vectorial training signal from said trained model of said controlled system, a control system-side network, and means for training said control system-side network in accordance with said second vectorial training signal, means for applying said state vector and said correcting variables vector to said control system-side network, said network being trained by said second vectorial training signal to provide therefrom a correction value to be applied to said optimal correcting variables vector to form an actual correcting variables vector applied to said controlled system.
10. A control system as claimed in claim 9, wherein said adaptive model of said controlled system comprises first matrix means representing a first matrix and second matrix means for representing a second matrix, means for multiplying said state vector by said first matrix, and means for multiplying said second matrix by said correcting variables vector, means for summing said state and correcting variables vectors each multiplied by the respective one of said matrices to provide an estimated model value of the time derivative of said state vector, and means for integrating said time derivative to provide an estimated model value of said state vector, and further comprising controlled system-side adaptive network means having an input and an output for generating at said output a correction value to be applied to said estimated model value of said state vector, and means for training said controlled system-side adaptive network means in accordance with said first vectorial training signal.
11. A control system as claimed in claim 9, comprising means for applying said first vectorial training signal to said output of said controlled system-side adaptive means to provide, at said input thereof, said second vectorial training signal by backpropagation.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. An integral alignment optic comprising:
a projection alignment component device on a first surface of a photolithography apparatus, the first surface also including a first set of features;
a target alignment component device on a second surface of a photolithography apparatus, the second surface also including a second set of features wherein at least one of the features in the second set correspond to one of the features of the first set, and the projection alignment component device corresponds to the target alignment component device so that offsets of an image of the projection alignment component device projected onto the target alignment component device indicate offsets between the corresponding features on the respective surfaces of the photolithography apparatus.
2. An integral alignment optic as defined in claim 1, wherein the photolithography apparatus is a metrology tool.
3. An integral alignment optic as defined in claim 1, wherein the image produced by the projection alignment component device is a holograph.
4. An integral alignment optic as defined in claim 1, wherein the image produced by the projection alignment component device is a spot.
5. An integral alignment optic as defined in claim 1, wherein the image produced by the projection alignment component device is a line.
6. An integral alignment optic as defined in claim 1, wherein the image produced by the projection alignment component device is a ring.
7. An integral alignment optic as defined in claim 1, wherein the projection alignment component on the projection component is a fresnel zone plate.
8. An integral alignment optic as defined in claim 1, wherein the projection component is a silicon wafer.
9. An integral alignment optic as defined in claim 1, wherein the projection component is a notched wafer.
10. An integral alignment optic as defined in claim 1, wherein the projection component is a flat panel display.
11. An integral alignment optic as defined in claim 1, wherein the projection component is a reticle.
12. An integral alignment optic as defined in claim 1, wherein the projection component is a mask.
13. An integral alignment optic as defined in claim 1, wherein the projection component is an electronic recording media.
14. An integral alignment optic as defined in claim 1, wherein the surface is a reticle.
15. An integral alignment optic as defined in claim 1, wherein the surface is a mask.
16. An integral alignment optic as defined in claim 1, wherein the surface is a semiconductor.
17. An integral alignment optic as defined in claim 1, wherein the surface is a silicon wafer.
18. An integral alignment optic as defined in claim 1, wherein the surface is a notched wafer.
19. An integral alignment optic as defined in claim 1, wherein the surface is a flat panel display.
20. An integral alignment optic as defined in claim 1, wherein the surface is an electronic CCD.
21. An integral alignment optic as defined in claim 1, wherein the second surface is a diode array.
22. An integral alignment optic as defined in claim 1, wherein the surface is a metal plate.
23. An integral alignment optic as defined in claim 1, wherein the surface is an electronic recording media.
24. An integral alignment optic as defined in claim 1, wherein the surface is an aperture plate.
25. A metrology tool comprising:
a reticle that includes a set of features and at least one projection alignment component device;
an aperture plate that includes a set of features wherein at least one of the features of the aperture plate correspond to one of the features of the reticle, the aperture plate also including at least one target alignment component device; wherein the projection alignment component device is imaged onto the target alignment component device such that offsets of the projection alignment device image at the target alignment component device indicates offsets between the corresponding features of the reticle and aperture plate, thereby indicating alignment between the two.
26. A method of optical registration, the method comprising:
projecting light through first surface that includes a first set of features and at least one projection alignment component device so as to produce an image at a second surface;
measuring a position of the image of the first set of features and projection alignment device at the second surface, wherein the second surface includes at least one feature that correspond to one of the features of the first set of features, the second surface also includes at least one target alignment component device; wherein the projection alignment component device is imaged onto the target alignment component device;
determining an offset of the projected alignment component device relative to the target alignment component device; and
using the measurement of the offset of the projected alignment component device and the target alignment component device to determine an offset of corresponding features on the respective surfaces.
27. A method as defined in claim 26, wherein the projection alignment component device produces a hologram.
28. A method as defined in claim 26, wherein the image is produced by projecting laser light through first set of features and the projection alignment component device.
29. A method as defined in claim 26, wherein the first surface is a reticle.
30. A method as defined in claim 26, wherein the second surface is an aperture plate.
31. A method as defined in claim 26, wherein the first surface is a flat panel display.
32. A method as defined in claim 26, wherein the second surface is a flat panel display.
33. A method as defined in claim 26, wherein the determined offsets are used in adjusting a separation between the first surface and the second surface.
34. A method as defined in claim 26, wherein the offset comprises calibration data.
35. A method as defined in claim 34, wherein the calibration data is used in an in-situ interferometer.
36. A method as defined in claim 26, wherein the projection alignment component device is a fresnel zone plate.
37. A method as defined in claim 26, wherein the target alignment component device is a hole.
38. A method of calibrating lens aberration, the method comprising:
receiving overlay data collected with a metrology tool;
receiving metrology tool calibration data, wherein the metrology tool includes a reticle that has a set of features and at least one projection alignment component device, the metrology tool also has an aperture plate that includes a set of features wherein at least one of the features in the aperture plate correspond to one of the features in the reticle, the aperture plate also includes at least one target alignment component device; wherein the projection alignment component device is imaged onto the target alignment component device such that offsets of the projection alignment device image at the target alignment component device indicates offsets between the corresponding features on the reticle and aperture plate; and
combining the overlay data and the calibration data to determine lens aberration data.
39. A method as defined in claim 38, wherein the overlay data is from an in-situ interferometer.
40. An apparatus for computing a calibration file for a metrology tool, the method comprising:
a data interface for receiving local offsets of features on surfaces and global offsets of the metrology tool, wherein the metrology tool includes a reticle that has a set of features and at least one projection alignment component device, the metrology tool also includes an aperture plate that includes a set of features wherein at least one of the features in the aperture plate correspond to one of the features in the reticle, the aperture plate also includes at least one target alignment component device; wherein the projection alignment component device is imaged onto the target alignment component device such that offsets of the projection alignment device image at the target alignment component device indicates offsets between the corresponding features on the reticle and aperture plate; and
a processor configured to combine the local offsets and global offset and outputting a calibration file of corrected positional values of feature-to-feature alignment.