System for estimating the occurrence of defects, and computer-readable medium
The purpose of this invention is to estimate the occurrence of defects such as probability pattern defects, with a small number of inspection points. To achieve this purpose, the present invention proposes a system and a computer-readable medium. The system comprises: a step in which first data pertaining to the probability that the edge of a pattern determined on the basis of measurement data for a plurality of measurement points on a wafer is present at a first position is acquired or is generated; a step in which, if the edge is at the first position, second data pertaining to the probability that a film defect covers a region including the first position and a second position which is different to said first position is acquired or generated; and a step in which the probability of the defect occurring is predicted on the basis of the first data and the second data.
1. A non-transitory computer-readable medium that stores a program instruction executable on a computer system to implement a computer-executed method for estimating an occurrence probability of a defect of a wafer based on measurement data obtained by a measurement tool, wherein
the computer-executed method includes
acquiring or generating first data pertaining to a probability that an edge of a pattern determined based on measurement data for a plurality of measurement points on the wafer is present at a first position,
acquiring or generating second data pertaining to a probability that a defect covers a region including the first position and a second position which is different from the first position when the edge is at the first position, and
predicting the occurrence probability of the defect based on a product of the first data and the second data.
2. The non-transitory computer-readable medium according to claim 1 , wherein
when an edge position coordinate relative to a design pattern of the first position is at x_edge, the first data is P 1 (x_edge), which is a frequency distribution of the x_edge obtained based on the measurement data of the plurality of measurement points.
3. The non-transitory computer-readable medium according to claim 1 , wherein
when the edge is at x_edge, the second data is a probability function P 2 (x, x_edge) that the film defect continuously occurs between the x_edge and the second position x.
4. The non-transitory computer-readable medium according to claim 3 , wherein
the probability function P 2 is derived by obtaining a local film defect probability P 3 per unit area based on a light intensity distribution obtained by an exposure device that projects a pattern on the wafer, and obtaining a direct product in a range from the x_edge to the second position x for the P 3 .
5. The non-transitory computer-readable medium according to claim 3 , wherein the probability function P 2 is derived by obtaining a probability that photoelectrons generated by photons of light projected on a resist film formed on the wafer by an exposure device that projects a pattern on the wafer generate secondary electrons in proximity between the x_edge and the second position x of the resist film.
6. The non-transitory computer-readable medium according to claim 1 , wherein
based on a measurement of a plurality of patterns formed on a wafer different from a wafer for which the occurrence probability of the defect is to be estimated, the first data and the occurrence probability of the defect are derived, and the second data are derived based on first data pertaining to the different wafer and a defect occurrence distribution.
7. The non-transitory computer-readable medium according to claim 6 , wherein
when an edge position coordinate relative to a design pattern of the first position is at x_edge, the first data is P 1 (x_edge), which is a frequency distribution of the x_edge obtained based on the measurement data of the plurality of measurement points.
8. The non-transitory computer-readable medium according to claim 7 , wherein
a learning model is generated that receives the P 1 (x_edge) and outputs the occurrence probability of the defect and in which a parameter learned by using teaching data is provided as the second data in an intermediate layer, and the occurrence probability of the defect is output by inputting the P 1 (x_edge) into the learning model.
9. The non-transitory computer-readable medium according to claim 6 , wherein
when the edge is at x_edge, the second data is a probability function P 2 (x, x_edge) that a film defect continuously occurs between the x_edge and the second position x.
10. The non-transitory computer-readable medium according to claim 6 , wherein
the wafer different from the wafer for which the occurrence probability of the defect is to be estimated and the wafer for which the occurrence probability of the defect is to be estimated are wafers having different manufacturing conditions.
11. A system configured to predict an occurrence probability of a defect on a wafer, the system comprising:
a measurement tool configured to generate an output based on detection of a signal obtained by irradiating the wafer with a beam; and
a computer configured to
acquire or generate first data pertaining to a probability that an edge of a pattern determined based on measurement data for a plurality of measurement points on the wafer is present at a first position,
acquire or generate second data pertaining to a probability that a film defect covers a region between the first position and a second position which is different from the first position when the edge is at the first position, and
obtain a product of the first data and the second data.
12. The system according to claim 11 , wherein
when an edge position coordinate relative to a design pattern of the first position is at x_edge, the first data is P 1 (x_edge), which is a frequency distribution of the x_edge obtained based on the measurement data of the plurality of measurement points.
13. The system according to claim 12 , wherein
the computer generates a learning model that receives the first data and outputs the occurrence probability of the defect and in which a parameter learned by using teaching data is provided as the second data in an intermediate layer, and outputs the occurrence probability of the defect by inputting the first data into the learning model.
14. The system according to claim 11 , wherein
when the edge is at x_edge, the second data is a probability function P 2 (x, x_edge) that a defect continuously occurs between the x_edge and the second position x.
15. The system according to claim 14 , wherein
the computer derives the probability function P 2 by obtaining a local film defect probability P 3 per unit area based on a light intensity distribution obtained by an exposure device that projects a pattern on the wafer, and obtaining a direct product in a range from the x_edge to the second position x for the P 3 .
16. The system according to claim 14 , wherein
the computer derives the probability function P 2 by obtaining a probability that photoelectrons generated by photons of light projected on a resist film formed on the wafer by an exposure device that projects a pattern on the wafer generate secondary electrons in proximity between the x_edge and the second position x of the resist film.
17. The system according to claim 11 , wherein
the computer is configured to output the second data by inputting the first data and an actually measured value of the occurrence probability of the defect.