WO2024100849A1

WO2024100849A1 - Aging condition evaluation device and setting method

Info

Publication number: WO2024100849A1
Application number: PCT/JP2022/041950
Authority: WO
Inventors: 貴則近藤; 美臣甲斐; 健一郎米田; 健次岡; 貴志堤; 真史佐藤; 将志川畑; 俊一松本
Original assignee: 株式会社日立ハイテク
Priority date: 2022-11-10
Filing date: 2022-11-10
Publication date: 2024-05-16

Abstract

The present invention provides an aging condition evaluation device for evaluating an aging condition that is a setting condition for aging processing by a process device. The aging condition evaluation device includes a specimen stage for supporting a specimen subjected to processing by the process device, an illumination optical system that irradiates the specimen placed on the specimen stage with illumination light, a plurality of detection optical systems that collect light from the specimen and perform conversion thereof into electrical signals and output detection signals, and a signal processing device that processes the detection signals from the plurality of detection optical systems. The signal processing device scans an initial specimen that is initially processed, out of one lot of specimens subjected to processing involving the aging processing by the process device, extracts a haze signal of the initial specimen, and determines the appropriateness of the aging condition by difference in comparison of the haze signal of the initial specimen with a reference haze signal.

Description

Apparatus and method for evaluating aging conditions

The present invention relates to an aging condition evaluation device and an aging condition setting method for evaluating the aging conditions of process equipment such as plasma etching equipment used in semiconductor manufacturing processes.

In a process device for processing semiconductor wafers, a case containing, for example, N (multiple) wafers may be set, and the N wafers in the case may be processed as one lot. For example, in a plasma etching device, the wafer is set in a chamber, a specific gas is supplied, and the gas is turned into plasma by discharging, and the wafer is etched with the generated plasma (Patent Document 1, etc.).

JP 2011-100865 A

For example, in a plasma etching apparatus, in order to perform stable processing, it is desirable for the chamber to be in a specified condition when processing the wafer. However, the intervals at which wafer lots are supplied to the plasma etching apparatus are not constant but vary, and the temperature of the chamber drops if the waiting time for the arrival of the next lot becomes long. If the subsequent lot is processed in a cooled chamber, the chamber condition may be ready when the Nth wafer is processed, but the first several wafers, especially the first wafer, will be processed before the chamber condition is ready. Such condition variations can also be caused by differences in the degree of gas filling in the chamber. As in the example of this plasma etching apparatus, in a process apparatus, there may be a difference in the chamber condition when the first and Nth wafers of a lot are processed. In this case, there will be a difference in the processing quality of the first and Nth wafers, and in severe cases, the first or first several wafers may be defective.

Therefore, in general, in process equipment, a process (referred to as an aging process in this specification) is performed to adjust the chamber conditions as necessary before processing the first wafer of a lot. For example, in a plasma etching device, a "run-in discharge" is performed while a specific gas is supplied to the chamber in an attempt to suppress the difference in plasma light emission state during processing of the first wafer and the Nth wafer.

However, the process conditions applied to the processing of wafers in the process equipment are initially set by the process equipment manufacturer or the user's process department for each process and even for each type of wafer (for each wafer manufacturing stage, reclaimed wafers, new wafers, etc.). For example, they are set by adjusting parameters such as the microwave output, plasma discharge time, and gas flow rate of the plasma etching equipment. In addition to the process conditions, the process equipment also sets the conditions for the aging process (hereinafter, aging conditions) that perform the acclimatization discharge during the waiting time mentioned above. The aging conditions must be set for each process condition. Some of the process condition parameters are adjusted and set for the acclimatization discharge. In addition, the aging conditions for adjusting the condition of the process equipment to a necessary and sufficient level will differ depending on the waiting time mentioned above, even if the corresponding process conditions are the same. In this way, like the process conditions, the aging conditions are selected and adjusted from a wide range of parameters depending on the process and type of wafer, which requires a great deal of effort and time.

The aging conditions are evaluated by, for example, comparing the quality of the first and Nth wafers from the same lot. For example, OCD (Optical Critical Dimension) may be used to compare the quality of wafers. In an OCD test, the test object is the CD (Critical Dimension), so the aging conditions cannot be evaluated using a wafer on which no pattern is formed. For this reason, when preparing wafers for evaluating aging conditions during the research and development stage of semiconductors, for example, it is necessary to manufacture patterned wafers, which is costly. Furthermore, CD values are measured only at a small part of the wafer in the case of CD-SEM using an electron beam, and at only a few dozen measurement points on the wafer surface in the case of OCD, so the processing state of the entire wafer cannot be confirmed uniformly, and there are cases where defects in the aging conditions cannot be detected depending on the selection of inspection points.

The object of the present invention is to provide an aging condition evaluation device and setting method that can accurately evaluate the aging conditions applied to a process device in a short period of time.

In order to achieve the above object, the present invention provides an aging condition evaluation device that evaluates the aging conditions, which are the setting conditions for the aging treatment of a process device, and includes a sample stage that supports a sample that has been treated in the process device, an illumination optical system that irradiates illumination light onto the sample placed on the sample stage, a plurality of detection optical systems that collect light from the sample, convert it into an electrical signal, and output a detection signal, and a signal processing device that processes the detection signals of the plurality of detection optical systems, and the signal processing device scans an initial sample that has been treated early among a lot of samples that have been treated with aging treatment in the process device, extracts a haze signal of the initial sample, and provides an aging condition evaluation device that judges whether the aging conditions are appropriate based on the difference when the haze signal of the initial sample is compared with a reference haze signal.

The present invention makes it possible to evaluate the aging conditions applied to process equipment with high accuracy in a short period of time.

FIG. 1 is a schematic diagram of a configuration example of an aging condition evaluation device according to a first embodiment of the present invention; Schematic diagram showing the sample scanning trajectory Schematic diagram showing the sample scanning trajectory Schematic diagram showing the attenuator A schematic diagram showing the positional relationship between the optical axis of illumination light guided obliquely to the surface of a sample and the illumination intensity distribution shape. A schematic diagram showing the positional relationship between the optical axis of illumination light guided obliquely to the surface of a sample and the illumination intensity distribution shape. A diagram showing the area where the detection optics collects scattered light, viewed from above. A schematic diagram showing the zenith angles of low-angle and high-angle detection optical systems. Plan view showing the azimuth angle of the low-angle detection optics Plan view showing the azimuth angle of the high-angle detection optics A schematic diagram showing an example of the configuration of the detection optical system FIG. 1 is a functional block diagram of a main part of a signal processing device provided in an aging condition evaluation device according to a first embodiment of the present invention; A flow chart showing typical steps for evaluating the aging conditions of process equipment in the semiconductor R&D to manufacturing process. 1 is a schematic flowchart showing an example of a flow of evaluating aging conditions of a process device. A schematic diagram showing the change in temperature over time in a chamber of a process device. A schematic diagram showing the effect on a sample as the temperature of a process device chamber changes over time. 1 is a flowchart showing the procedure of an aging condition evaluation process performed by an aging condition evaluation device according to a first embodiment of the present invention. Conceptual diagram of machine learning according to a second embodiment of the present invention FIG. 13 is a schematic diagram showing a main part of an aging condition evaluation device according to a third embodiment of the present invention; FIG. 13 is a schematic diagram for explaining the main functions of an aging condition evaluation device according to a fourth embodiment of the present invention;

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
The aging condition evaluation device (hereinafter, appropriately abbreviated as evaluation device) described in each of the following embodiments scans a sample, here a wafer (bare wafer, film-coated wafer, patterned wafer, etc.) during the semiconductor manufacturing process, and evaluates the aging condition from the wafer state. As an example of this evaluation device, an optical defect inspection device that inspects defects on a sample is used. The optical defect inspection device outputs the number, coordinates, type, etc. of defects attached to or formed on the wafer as a signal based on reflected light or scattered light obtained by scanning the sample. The optical defect inspection device has a very fast scan speed compared to diagnostic devices that use electron beams, X-rays, etc. as a light source. Therefore, while diagnostic devices that use electron beams, X-rays, etc. as an energy source can only measure a very small number of points on the sample due to time constraints, the optical defect inspection device, especially the dark-field inspection device that uses scattered light, can scan the entire surface of the sample and evaluate the aging condition from the state of the entire surface of the sample. The light-based signal obtained by scanning the sample with this optical defect inspection device includes not only defect signals used for defect detection, but also a signal called a haze signal. If a signal with a fluctuation frequency (time fluctuation of signal intensity) higher than a certain value is considered to be a high frequency, and a signal with a lower frequency is considered to be a low frequency, then a defect signal corresponds to a high frequency component among signals based on light obtained from a sample. Conversely, a haze signal corresponds to a low frequency component. For example, a signal caused by relatively large irregularities attached to the sample, such as foreign matter, is likely to be detected as a high frequency component, while a signal caused by the characteristics of the wafer itself, such as the thickness of a film on the sample or extremely small irregularities (roughness) on the sample surface, is likely to be detected as a low frequency component.

The evaluation device in each embodiment has a unique function of evaluating the conditions of the aging treatment (aging conditions) applied to the process equipment that processed the inspected sample, based on a haze signal obtained by scanning the entire surface of a sample that was processed in the process equipment along with an aging treatment. The evaluation device utilizes a haze signal that is not generally used (removed) in sample defect inspection, and evaluates the aging conditions of the process equipment that processed the sample based on the haze signal obtained for the sample.

In the evaluation device, consider the case where a haze signal is acquired for one lot (N sheets) of samples processed in a process device with aging treatment. "With aging treatment" means that the lot was processed immediately after the aging treatment without processing another lot after the aging treatment. N is a natural number of 2 or more, for example, about 25. In this case, if there is a difference in the value or distribution of the haze signal between the first sample and the last Nth sample, it is presumed that the chamber conditions in the process device were different between the first and Nth samples. Furthermore, if the aging conditions in the process device are not appropriate, in the processing of a lot with aging treatment, the first sample may be processed before the conditions of the process device (chamber temperature, gas saturation, etc.) are ready. Even in this case, the chamber does not reach the desired condition when the first sample is processed, but the chamber reaches the desired condition as the second and third samples are processed. However, while most of the samples in the lot are processed well, the first one or two samples may not be processed sufficiently. If the processing state falls below the allowable level, the sample is deemed defective. Such differences in the chamber conditions in the process equipment will affect the quality of the sample after processing, for example, if the sample is processed in a plasma etching equipment, the differences will be significant and will appear in the CD value, surface roughness, and surface film thickness of the sample. However, in the case of equipment that measures the CD value, surface roughness, and surface film thickness, users often measure one or several wafers in one lot by sampling, and many wafers are not measured and sent to the next process, due to the cost of measurement, especially in measurement equipment with slow scan speeds. In addition, the effect of inappropriate aging conditions on the processed sample may appear as a small, complex, or other condition difference that cannot be captured as the CD value, surface roughness, or surface film thickness. In this case, it is not possible to confirm the appropriateness of the aging conditions after processing in the process equipment. In the evaluation equipment of each embodiment, taking advantage of the advantage of being able to scan the entire surface at high speed, the appropriateness of the aging conditions that appear on the sample is evaluated based on the haze signal.

Whether the desired condition is reached from the first wafer, the second, third, or fifth wafer depends on the aging conditions that have been set. Therefore, the first one or several wafers in a lot that are processed in a chamber that has not reached the desired condition due to inappropriate aging conditions are called "initial wafers," and it should be noted that this is not limited to just the first or second wafers.

Specific examples of evaluation equipment are described below.

(First embodiment)
- Aging condition evaluation device -
Fig. 1 is a schematic diagram of an example of the configuration of an aging condition evaluation device according to a first embodiment of the present invention. An XYZ orthogonal coordinate system with the Z axis extending vertically is defined as shown in Fig. 1. The evaluation device 100 inspects a sample W and detects defects such as adhesion of foreign matter and abnormal film formation on the surface of the sample W. The evaluation device 100 is a rotary scanning type device that scans the sample W by rotating it in a circumferential direction (θ direction) and moving it in a radial direction (r direction).

The evaluation device 100 includes a stage ST, an illumination optical system A, a plurality of detection optical systems Bn (n=1, 2, etc.), sensors Cn, Cn' (n=1, 2, etc.), a signal processing device D, a memory device DB, a control device E1, an input device E2, and a monitor E3.

-stage-
The stage ST is an apparatus including a sample stage ST1 and a scanning device ST2. The sample stage ST1 is a stage that supports a sample W that has been processed in a process device such as a plasma etching device. The scanning device ST2 is an apparatus that drives the sample stage ST1 to change the relative position between the sample W and the illumination optical system A, and is configured to include a translation stage, a rotation stage, and a Z stage, although detailed illustration is omitted. A rotation stage is mounted on the translation stage via the Z stage, and the sample stage ST1 is supported on the rotation stage. The translation stage translates in the horizontal direction together with the rotation stage. The rotation stage rotates (spins) around a rotation axis that extends vertically. The Z stage serves to adjust the height of the surface of the sample W.

FIG. 2 is a schematic diagram showing the scanning trajectory of the sample W by the scanning device ST2. As will be described later, the beam spot BS, which is the incident area of the illumination light emitted from the illumination optical system A on the surface of the sample W, is a minute point with a long illumination intensity distribution in one direction as shown in the figure. The long axis direction of the beam spot BS is s2, and the direction intersecting the long axis (for example, the short axis direction perpendicular to the long axis) is s1. The sample W rotates with the rotation of the rotating stage, and the beam spot BS is scanned in the s1 direction relative to the surface of the sample W, and the sample W moves in the horizontal direction with the translation stage translation, and the beam spot BS is scanned in the s2 direction relative to the surface of the sample W. The beam spot BS moves in the s2 direction by a distance equal to or less than the length of the beam spot BS in the s2 direction during one rotation of the sample W. By such an operation of the scanning device ST2, the sample W rotates and translates, as shown in FIG. 2, and the beam spot BS moves in a spiral trajectory from the center of the sample W to the outer edge or its vicinity, and the entire surface of the sample W is scanned.

The stage ST may be configured to have another translation stage, whose axis of movement extends in a direction intersecting the axis of movement of the translation stage in a horizontal plane, in place of (or in addition to) the rotation stage. In this case, as shown in FIG. 3, the beam spot BS scans the surface of the sample W by folding over a linear trajectory instead of a spiral trajectory. In the example shown in the figure, the first translation stage is driven in translation at a constant speed in the s1 direction, the second translation stage is driven in the s2 direction by a predetermined distance (for example, a distance equal to or less than the length of the beam spot BS in the s2 direction), and then the first translation stage is turned back in the s1 direction and driven in translation again. This causes the beam spot BS to repeat linear scanning in the s1 direction and movement in the s2 direction, scanning the entire surface of the sample W. Compared to this XY scanning method, the rotation scanning method of FIG. 2 does not involve a reciprocating motion that is repeatedly accelerated and decelerated, so the inspection time of the sample W can be shortened.

- Illumination optical system -
The illumination optical system A shown in Fig. 1 includes a group of optical elements for irradiating a desired illumination light onto a sample W placed on a sample stage ST1. As shown in Fig. 1, the illumination optical system A includes a laser light source A1, an attenuator A2, an emitted light adjustment unit A3, a beam expander A4, a polarization control unit A5, a focusing optical unit A6, reflection mirrors A7-A9, etc.

Laser Light Source The laser light source A1 is a unit that emits a laser beam as illumination light. When the evaluation device 100 detects minute defects near the surface of the sample W, the laser light source A1 is a unit that emits a high-output laser beam of 2 W or more in ultraviolet or vacuum ultraviolet with a short wavelength (wavelength of 355 nm or less) that does not easily penetrate into the inside of the sample W. When the evaluation device 100 detects defects inside the sample W, the laser light source A1 is a unit that emits a visible or infrared laser beam with a long wavelength that easily penetrates into the inside of the sample W.

Attenuator FIG. 4 is a schematic diagram showing the attenuator A2. The attenuator A2 is a unit that attenuates the light intensity of the illumination light from the laser light source A1. In this embodiment, the attenuator A2 is a combination of a first polarizing plate A2a, a half-wave plate A2b, and a second polarizing plate A2c. The half-wave plate A2b is configured to be rotatable around the optical axis of the illumination light. The illumination light incident on the attenuator A2 is converted into linearly polarized light by the first polarizing plate A2a, and then the polarization direction is adjusted to the slow axis azimuth angle of the half-wave plate A2b and passes through the second polarizing plate A2c. The light intensity of the illumination light is attenuated at an arbitrary ratio by adjusting the azimuth angle of the half-wave plate A2b. If the degree of linear polarization of the illumination light incident on the attenuator A2 is sufficiently high, the first polarizing plate A2a can be omitted. For the attenuator A2, an attenuator in which the relationship between the incident illumination light and the light attenuation rate is calibrated in advance is used. It should be noted that the attenuator A2 is not limited to the configuration illustrated in FIG. 4, but can also be configured using an ND filter having a gradation density distribution, and can be configured such that the attenuation effect can be adjusted by combining multiple ND filters having different densities.

Emitted light adjustment unit The emitted light adjustment unit A3 shown in FIG. 1 is a unit that adjusts the angle of the optical axis of the illumination light attenuated by the attenuator A2, and in this embodiment, it is configured to include multiple reflecting mirrors A3a and A3b. The reflecting mirrors A3a and A3b are configured to sequentially reflect the illumination light, but in this embodiment, the incident and exit surfaces of the illumination light to the reflecting mirror A3a are configured to be perpendicular to the incident and exit surfaces of the illumination light to the reflecting mirror A3b. The incident and exit surfaces are surfaces that include the optical axis of the light incident on the reflecting mirror and the optical axis of the light emitted from the reflecting mirror. In a configuration in which the illumination light is incident on the reflecting mirror A3a in the +X direction, which is different from the schematic FIG. 1, for example, the illumination light changes its traveling direction to the +Y direction by the reflecting mirror A3a and then to the +Z direction by the reflecting mirror A3b. In this example, the incident and exit surfaces of the illumination light to the reflecting mirror A3a are the XY plane, and the incident and exit surfaces to the reflecting mirror A3b are the YZ plane. The reflecting mirrors A3a and A3b are provided with a mechanism (not shown) for translating the reflecting mirrors A3a and A3b and a mechanism (not shown) for tilting the reflecting mirrors A3a and A3b. The reflecting mirrors A3a and A3b move, for example, in parallel in the incident or outgoing direction of the illumination light with respect to themselves, and tilt around the normal to the incident and outgoing surfaces. This allows the offset amount and angle in the XZ plane and the offset amount and angle in the YZ plane to be independently adjusted for the optical axis of the illumination light emitted in the +Z direction from the outgoing light adjustment unit A3. In this embodiment, a configuration using two reflecting mirrors A3a and A3b is illustrated, but a configuration using three or more reflecting mirrors may be used.

Beam Expander The beam expander A4 is a unit that expands the diameter of the luminous flux of the incident illumination light, and has a plurality of lenses A4a and A4b. An example of the beam expander A4 is a Galilean type that uses a concave lens as the lens A4a and a convex lens as the lens A4b. The beam expander A4 is provided with a mechanism for adjusting the distance between the lenses A4a and A4b (zoom mechanism), and the expansion rate of the luminous flux diameter changes by adjusting the distance between the lenses A4a and A4b. If the illumination light incident on the beam expander A4 is not a parallel luminous flux, collimation (quasi-parallelization of the luminous flux) is also possible by adjusting the distance between the lenses A4a and A4b. However, the collimation of the luminous flux may be performed by a collimating lens installed upstream of the beam expander A4 separately from the beam expander A4.

Beam expander A4 is installed on a translation stage with two or more axes (two degrees of freedom) and is configured so that its position can be adjusted so that its center coincides with the incident illumination light. Beam expander A4 also has a swing angle adjustment function with two or more axes (two degrees of freedom) so that the incident illumination light coincides with the optical axis.

Although not specifically shown, the state of the illumination light entering the beam expander A4 is measured by a beam monitor midway along the optical path of the illumination optical system A.

Polarization control unit The polarization control unit A5 is an optical system that controls the polarization state of the illumination light, and is configured to include a half-wave plate A5a and a quarter-wave plate A5b. For example, when a reflecting mirror A7 described later is inserted in the optical path to illuminate the sample W obliquely, the amount of scattered light from the surface of the sample W can be increased compared to polarized light other than P-polarized light by making the illumination light P-polarized by the polarization control unit A5. When an oxide film is present on the surface of the sample W, the amount of scattered light from the sample surface can be increased more than P-polarized light by using S-polarized light depending on the material and thickness of the film. By selecting the polarization according to the sample W, it is possible to switch between conditions under which haze light is likely to occur and conditions under which it is difficult to occur, thereby improving the sensitivity of defect inspection and improving the sensitivity of haze light to sample characteristics. For example, when the state of the sample W is evaluated using the output of haze light, it is advantageous to use S-polarized illumination light. It is also possible to use the polarization control unit A5 to make the illumination light circularly polarized or 45-degree polarized light intermediate between P-polarized light and S-polarized light.

Reflecting Mirror As shown in FIG. 1, the reflecting mirror A7 is moved in parallel in the direction of the arrow by a driving mechanism (not shown) and enters and exits the optical path of the illumination light toward the sample W. This switches the incidence path of the illumination light to the sample W. By inserting the reflecting mirror A7 into the optical path, the illumination light emitted from the polarization control unit A5 as described above is reflected by the reflecting mirror A7 and enters the sample W obliquely via the focusing optical unit A6 and the reflecting mirror A8. In this specification, the illumination light is made to enter the sample W from a direction oblique to the normal to the surface of the sample W in this way, which is referred to as "oblique incidence illumination". On the other hand, when the reflecting mirror A7 is removed from the optical path, the illumination light emitted from the polarization control unit A5 is made to enter the sample W perpendicularly via the reflecting mirror A9, the polarizing beam splitter B'3, the polarization control unit B'2, the reflecting mirror B'1, and the detection optical system B3. In this specification, the illumination light is made to enter the sample W perpendicularly to the surface of the sample W in this way, which is referred to as "vertical illumination".

FIGS. 5 and 6 are schematic diagrams showing the positional relationship between the optical axis of the illumination light guided obliquely to the surface of the sample W by the illumination optical system A and the illumination intensity distribution shape. FIG. 5 shows a schematic cross-section of the sample W cut at the plane of incidence of the illumination light incident on the sample W. FIG. 6 shows a schematic cross-section of the sample W cut at a plane that is perpendicular to the plane of incidence of the illumination light incident on the sample W and includes the normal to the surface of the sample W. The plane of incidence is a plane that includes the optical axis OA of the illumination light incident on the sample W and the normal to the surface of the sample W. Note that FIGS. 5 and 6 show only a portion of the illumination optical system A, and for example, the exit light adjustment unit A3 and the reflecting mirrors A7 and A8 are not shown.

As mentioned above, when the reflecting mirror A7 is inserted into the optical path, the illumination light emitted from the laser light source A1 is collected by the collecting optical unit A6, reflected by the reflecting mirror A8, and obliquely incident on the sample W. In this way, the illumination optical system A is configured to make the illumination light obliquely incident on the surface of the sample W. This oblique incidence illumination has its light intensity adjusted by the attenuator A2, its light beam diameter adjusted by the beam expander A4, and its polarization adjusted by the polarization control unit A5, so that the illumination intensity distribution is uniform within the incident surface. As shown in the illumination intensity distribution (illumination profile) LD1 shown in Figure 5, the beam spot formed on the sample W has a Gaussian light intensity distribution in the s2 direction.

In the plane perpendicular to the incident plane and the sample surface, the beam spot has a light intensity distribution with weak intensity at the periphery relative to the center of the optical axis OA, as shown in the illumination intensity distribution (illumination profile) LD2 in Figure 6. This light intensity distribution is, for example, a Gaussian distribution that reflects the intensity distribution of the light incident on the focusing optical unit A6, or an intensity distribution similar to a first-order Bessel function of the first kind or a sinc function that reflects the aperture shape of the focusing optical unit A6.

In addition, the angle of incidence of the oblique incidence illumination on the sample W (the tilt angle of the incident optical axis with respect to the normal to the sample surface) is adjusted to an angle suitable for detecting minute defects by adjusting the positions and angles of the reflecting mirrors A7 and A8. The angle of the reflecting mirror A8 is adjusted by an adjustment mechanism A8a. For example, the greater the angle of incidence of the illumination light on the sample W (the smaller the illumination elevation angle between the sample surface and the incident optical axis), the weaker the haze light that becomes noise in relation to the scattered light from minute defects on the sample surface.

-Detection optical system-
The detection optical system Bn (n=1, 2, . . . ) is a unit that collects scattered light from the sample surface, and is configured to include a plurality of optical elements including a collecting lens (objective lens). The n in the detection optical system Bn is the number of detection optical systems, and the evaluation device 100 of this embodiment is equipped with 13 detection optical systems (n=13). Hereinafter, when the detection optical system Bn is described without special notice, it means any detection optical system among the detection optical systems B1-B13. The same applies to the sensors Cn and Cn'. However, the number of detection optical systems Bn is not limited to 13 and can be increased or decreased as appropriate. In addition, the layout of the aperture (objective lens) of the detection optical system Bn can also be changed as appropriate.

Fig. 7 is a diagram showing the area where the detection optical system Bn collects scattered light as viewed from above, which corresponds to the arrangement of each objective lens of the detection optical system Bn. Fig. 8 is a diagram showing the zenith angles of the low-angle and high-angle optical systems of the detection optical system Bn, Fig. 9 is a plan view showing the azimuth angle of the low-angle detection optical system, and Fig. 10 is a plan view showing the azimuth angle of the high-angle detection optical system.

In the following explanation, the incident direction of the oblique incidence illumination on the sample W is used as a reference, and the traveling direction of the incident light with respect to the beam spot BS on the surface of the sample W when viewed from above (to the right in Figure 7) is referred to as the front, and the opposite direction (to the left in the same figure) is referred to as the rear. The lower side in the figure with respect to the beam spot BS is the right side, and the upper side is the left side. Furthermore, the angle φ2 (Figure 8) that the incident optical axis (center line of the aperture) of each detection optical system Bn makes with the normal N (Figure 8) of the sample W that passes through the beam spot BS is described as the zenith angle. Furthermore, the angle φ1 (Figures 9 and 10) that the incident optical axis (center line of the aperture) of each detection optical system Bn makes with the incident plane of the oblique incidence illumination in a planar view is described as the azimuth angle.

As shown in Figures 7 to 10, the detection optical systems Bn are arranged so that their directions (azimuth angle φ1 and zenith angle φ2) relative to the beam spot BS are different. In this embodiment, the objective lenses (apertures α1-α6, β1-β6, γ) of the detection optical system Bn are arranged along the upper hemispherical surface of a sphere (celestial sphere) centered on the beam spot BS on the sample W. The light incident on the apertures α1-α6, β1-β6, γ is focused by the corresponding detection optical system Bn.

Aperture γ overlaps the zenith (intersects with normal N) and is located directly above the beam spot BS formed on the surface of sample W.

　The openings α1-α6 are opened at a low angle so as to equally divide an annular area surrounding 360 degrees around the beam spot BS. The openings α1-α6 are arranged in the order of openings α1, α2, α3, α4, α5, α6 in a counterclockwise direction from the incident direction of the oblique incidence illumination in a plan view. The openings α1-α6 are also laid out to avoid the incident light path of the oblique incidence illumination and the regular reflection light path. The openings α1-α3 are arranged on the right side of the beam spot BS, the opening α1 is located to the right rear of the beam spot BS, the opening α2 is located to the right, and the opening α3 is located to the right front. The openings α4-α6 are arranged on the left side of the beam spot BS, the opening α4 is located to the left front of the beam spot BS, the opening α5 is located to the left, and the opening α6 is located to the left rear. The arrangement of the openings α4, α5, α6 is symmetrical to the openings α3, α2, α1 with respect to the incident plane of the oblique incidence illumination.

Apertures β1-β6 are opened so as to equally divide an annular area surrounding 360 degrees around beam spot BS at high angles (between apertures α1-α6 and aperture γ). Apertures β1-β6 are arranged in the order of apertures β1, β2, β3, β4, β5, β6 in a counterclockwise direction from the incidence direction of oblique incidence illumination in a plan view. Of apertures β1-β6, apertures β1 and β4 are laid out at a position that intersects with the incidence plane, aperture β1 is located rearward relative to beam spot BS, and aperture β4 is located forward. Apertures β2 and β3 are arranged on the right side of beam spot BS, aperture β2 is located to the rear right of beam spot BS, and aperture β3 is located to the front right. Apertures β5 and β6 are arranged on the left side of beam spot BS, aperture β5 is located to the front left of beam spot BS, and aperture β6 is located to the rear left.

The scattered light from the beam spot BS in various directions enters the apertures α1-α6, β1-β6, and γ, and is collected by the detection optical system Bn and guided to the corresponding sensors Cn and Cn'.

FIG. 11 is a schematic diagram showing an example of the configuration of the detection optical system. In the evaluation device 100 of this embodiment, each detection optical system Bn (or a part of the detection optical system) is configured as shown in FIG. 11, and the polarization direction of the scattered light that is transmitted can be controlled by the polarizing plate Bb. Specifically, the detection optical system Bn includes an objective lens (collecting lens) Ba, a polarizing plate Bb, a polarizing beam splitter Bc, imaging lenses (tube lenses) Bd, Bd', field stops Be, Be', and sensors Cn, Cn'.

The scattered light incident on the detection optical system Bn from the sample W is collected and collimated by the objective lens Ba, and its polarization direction is controlled by the polarizing plate Bb. The polarizing plate Bb is a half-wave plate that can be rotated by a driving mechanism (not shown). The driving mechanism is controlled by the control device E1, and the polarization direction of the scattered light incident on the sensor is controlled by adjusting the rotation angle of the polarizing plate Bb.

The scattered light, whose polarization has been controlled by the polarizing plate Bb, has its optical path split by the polarizing beam splitter Bc according to the polarization direction and enters the imaging lenses Bd and Bd'. The combination of the polarizing plate Bb and the polarizing beam splitter Bc cuts linearly polarized light components in any direction. To cut any polarized light component, including elliptically polarized light, the polarizing plate Bb is composed of a quarter-wave plate and a half-wave plate that can be rotated independently of each other.

The scattered illumination light that passes through the imaging lens Bd and is collected is photoelectrically converted by the sensor Cn via the field diaphragm Be, and the detection signal is input to the signal processing device D. The scattered illumination light that passes through the imaging lens Bd' and is collected is photoelectrically converted by the sensor Cn' via the field diaphragm Be', and the detection signal is input to the signal processing device D. The field diaphragms Be, Be' are installed so that their centers are aligned with the optical axis of the detection optical system Bn, and cut out light generated from positions other than the position to be inspected, such as light generated from positions away from the center of the beam spot BS of the sample W and stray light generated inside the detection optical system Bn. This has the effect of suppressing noise that interferes with defect detection.

The above configuration makes it possible to simultaneously detect two mutually orthogonal polarized components of scattered light generated at the same coordinates, which is effective when detecting multiple types of defects or haze light with different polarization characteristics.

When constructing the objective lens Ba from multiple closely-spaced lenses, in order to reduce loss of detected light due to gaps between the lenses, the outer periphery of the objective lens Ba may be cut out so as not to interfere with the sample W or other objective lenses, as in the example of Figure 11.

-Sensor-
The sensors Cn and Cn' convert the scattered light collected by the corresponding detection optical system into an electric signal and output the detection signal. The sensors C1 (C1'), C2 (C2'), C3 (C3') ... correspond to the detection optical systems B1, B2, B3 .... For these sensors Cn and Cn', single-pixel point sensors such as photomultiplier tubes and SiPM (silicon photomultiplier tubes) that photoelectrically convert weak signals with high gain can be used. In addition, sensors in which multiple pixels are arranged one-dimensionally or two-dimensionally, such as CCD sensors, CMOS sensors, and PSDs (position sensing detectors), may be used for the sensors Cn and Cn'. The detection signals output from the sensors Cn and Cn' are input to the signal processing device D as needed.

-Control device-
The control device E1 is a computer that controls the evaluation device 100, and includes a processing device (arithmetic control device) such as a CPU, a GPU, and an FPGA in addition to a ROM, a RAM, and other storage devices. The control device E1 is connected to the input device E2, the monitor E3, and the signal processing device D by wire or wirelessly. The input device E2 is a device through which a user inputs settings of inspection conditions, etc. to the control device E1, and various input devices such as a keyboard, a mouse, and a touch panel can be appropriately adopted. The control device E1 receives the output of the encoder of the rotation stage and the translation stage (rθ coordinates of the beam spot BS on the sample), and the inspection conditions input by the operator via the input device E2. The inspection conditions include the type, size, shape, material, illumination conditions, detection conditions, etc. of the sample W, as well as, for example, the sensitivity settings of each sensor Cn, Cn', and gain values and threshold values used for defect judgment and aging condition evaluation (evaluation of the aging conditions of the process device).

The control device E1 also outputs command signals to command the operation of the stage ST, illumination optical system A, etc. according to the inspection conditions, and outputs coordinate data of the beam spot BS synchronized with the defect detection signal to the signal processing device D. The control device E1 also displays and outputs an inspection condition setting screen and sample inspection data (inspection image, etc.) on the monitor E3. In addition to the final inspection result obtained by combining the signals from each sensor Cn, Cn', the inspection data can also display the individual inspection results from these sensors Cn, Cn'.

Also, as shown in Figure 1, the control device E1 may be connected to a Review SEM (Review Scanning Electron Microscope), which is an electron microscope used for defect inspection. In this case, the control device E1 can receive data on the defect inspection results from the Review SEM and transmit it to the signal processing device D.

The control device E1 can be configured as a single computer that forms a unit with the main body of the evaluation device 100 (stage, illumination optical system, detection optical system, sensor, etc.), but it can also be configured as multiple computers connected via a network. For example, the inspection conditions can be input to a computer connected via a network, and a computer attached to the main body of the device can be configured to control the main body of the device and the signal processing device D.

- Signal processing device -
The signal processing device D is a computer having a function of processing detection signals input from the sensors Cn, Cn' of the detection optical system Bn to detect defects in the sample W. The signal processing device D is configured to include a memory D1 (FIG. 12) including at least one of RAM, ROM, HDD, SSD, and other storage devices, as well as a processing device such as a CPU, GPU, or FPGA, just like the control device E1. This signal processing device D can be configured as a single computer that forms a unit with the main body of the evaluation device 100 (stage, illumination optical system, detection optical system, sensor, etc.), but can also be configured as multiple computers connected by a network. For example, a configuration can be adopted in which a computer attached to the main body of the device acquires defect detection signals from the main body of the device, processes the detection data as necessary and transmits it to a server, and the server executes processes such as defect detection and classification. A configuration in which the signal processing device D and the control device E1 are both performed by a single computer is also conceivable.

FIG. 12 is an example of a functional block diagram of the main parts of a signal processing device D provided in an aging condition evaluation device according to a first embodiment of the present invention. As shown in FIG. 12, the signal processing device D is provided with a memory D1, a defect determination circuit D2, a low-pass filter circuit D3, and an aging condition evaluation circuit D4.

While scanning the sample W, the signal processing device D receives detection signals (scattered light intensity signals) from the sensors Cn and Cn', and the encoder output of the stage ST (rθ coordinate of the beam spot BS on the sample) from the control device E1. In the signal processing device D, these detection signals and encoder outputs are associated with each other and recorded in the memory D1.

The defect judgment circuit D2 reads out the detection signals input from the sensors Cn, Cn' from the memory D1 in chronological order, sequentially judges whether these detection signals are defect signals indicating detected defects, records the judgment results in the memory D1 or the storage device DB, and also outputs them to the control device E1. In the defect judgment circuit D2, for example, high-frequency components of the detection signals are extracted as defect signals relating to defects such as foreign matter. High-frequency components are components with high fluctuating frequencies, specifically components whose time fluctuations exceed a preset value. The control device E1 displays and outputs the judgment results on the monitor E3 automatically, or in response to operation signals from the input device E2 input in conjunction with the operation of the operator.

The low-pass filter circuit D3 reads out the detection signals from the sensors Cn and Cn' from the memory D1 in chronological order, extracts the haze signals excluding the defect signals for each region of the sample W, and creates a haze map, which is the light intensity distribution of the entire surface, by adding coordinate information to the haze signals on the surface of the sample W. The haze signal refers to the low-frequency components of the light signals obtained from the sample, and is a signal that is mainly caused by the characteristics of the sample. Here, for example, the low-frequency components of the detection signal, that is, components whose fluctuation frequency (time fluctuation of the signal intensity) is lower than a preset value, are extracted as the haze signal. Since the optical defect inspection device is capable of high-speed scanning, it is possible to extract the haze signal for the entire surface of the sample, or a haze map based on it. However, there may be cases where it is desired to diagnose only a part of the sample, rather than the entire surface. In this case, the region from which the haze signal is extracted may be a sampling point, or the haze signal may be extracted by dividing the region into regions partitioned by a mesh of any mesh size.

When extracting haze signals by dividing into regions defined by a mesh like mesh, multiple haze signals are obtained for each region. The statistical values (average, median, etc.) of these multiple haze signals can be used as the haze signal for that region. One side of the mesh that divides the region can be set to, for example, about 1 mm to several mm. Although it depends on the size of the mesh, for example, with a 1 mm mesh, the sample surface is divided into more than 60,000 regions, and a detailed haze map is generated. Therefore, the haze map contains the intensity data of the haze light for each region of the sample W. However, the smaller the mesh size, the better, and by setting the mesh to a large size within a necessary and sufficient range, the calculation load on the signal processing device D can be reduced according to the reduction in the number of regions on the sample surface.

The aging condition evaluation circuit D4 evaluates the aging conditions of the process equipment that processed the sample W based on the haze signal extracted by the low-pass filter circuit D3. The aging conditions are evaluated by comparing the haze signal of the entire surface or each region of at least the first sample W processed in one lot of samples with aging processing in the process equipment with the reference haze signal of the corresponding region. The comparison may be made in the form of a haze map. In other words, the signal processing device D detects the change in the condition of the chamber of the process equipment, which appears as the microscopic surface shape of the first sample W, based on the difference between the haze signal and the reference haze signal. However, it is not necessary to exclude the comparison process between the haze signal of the second or subsequent sample W processed and the reference haze signal.

Specifically, the haze signal for the entire surface or each divided region of the first sample W is compared with the reference haze signal for the corresponding region, and if the difference between the haze signal and the reference haze signal exceeds a preset value, or if the number of regions exceeds an allowable value, a defect in the aging conditions is presumed. An algorithm can also be employed that generates a difference image between a haze map related to the haze signal obtained from sample W and a haze map related to the reference haze signal, and makes a similar judgment based on the difference (brightness difference) between the haze signal and the reference haze signal.

The aging condition evaluation circuit D4 displays and outputs information on the monitor E3 regarding the evaluation results of the aging conditions applied to the process equipment. For example, the evaluation results of the aging conditions are recorded in the memory D1 or the storage device DB, and are also output to the control device E1. The control device E1 displays and outputs the evaluation results on the monitor E3 automatically or in response to an operation signal from the input device E2 input in conjunction with the operation of the operator.

In addition, from the viewpoint of early detection of deficiencies in the aging conditions of the process equipment, it is desirable to evaluate the aging conditions by sequentially processing the detection signals input from the sensors Cn, Cn' in association with the defect inspection of the sample W by the signal processing device D. However, it is also possible to configure the system so that the detection signals acquired by scanning the entire surface of the sample W are temporarily stored in the storage device DB, and the stored data is post-processed at a desired timing (for example, at a fixed time every day) to evaluate the aging conditions.

In addition, the aging condition evaluation circuit D4 has a function of setting and presenting appropriate aging conditions for any process equipment. This function is useful when determining new appropriate aging conditions, for example, when designing a new semiconductor wafer manufacturing line, when introducing a new process equipment into an existing manufacturing line, when manufacturing new semiconductors on an existing manufacturing line, etc. When setting appropriate aging conditions, the aging condition evaluation circuit D4 evaluates and compares multiple aging conditions. Specifically, when determining new aging conditions, the aging condition evaluation circuit D4 compares the haze signal of the first sample W of each of multiple lots processed with aging treatments with different conditions in the process equipment with the reference haze signal. The aging conditions applied to each of these multiple lots become candidates for the appropriate aging conditions to be finally set. The candidates for appropriate aging conditions are, for example, multiple aging conditions that add variation conditions set by appropriately adjusting each parameter of the basic conditions to the basic conditions that have been identified as appropriate. Then, the aging condition evaluation circuit D4 sets the aging conditions applied to the lot to which the sample W belongs, for which the difference between the haze signal and the reference haze signal is within the tolerance and is smallest, as the appropriate aging conditions, and displays and outputs the same on, for example, the monitor E3.

- Reference haze signal -
In this embodiment, the reference haze signal used to evaluate the aging condition is defined for each of the sensors Cn and Cn' for the entire surface of the sample W or for each predetermined region (rθ coordinate in this embodiment) and is stored in, for example, a storage device DB. That is, a reference haze map is prepared for each of the sensors C and C'. The reference haze signal defined for the same sensor may be different for each region of the sample W.

For example, the reference haze signal may be an actual measurement value obtained by scanning the last sample W (the Nth sample W) of a lot of samples W aged under the aging conditions to be evaluated. The reference haze signal may be not only the last sample W processed, but also a value obtained by measuring an arbitrary sample W in the latter half of the lot when the conditions of the process equipment are stable, or a statistical value (average value, median value, etc.) of the actual measurement values of multiple samples W processed in the latter half (including the last). Furthermore, the reference haze signal may be, for example, obtained by scanning a reference sample. The reference sample is a sample that meets the standards in quality inspection, and is preferably a sample of the same type as the sample W and in the same process as the sample W. In addition, the reference haze signal may be set by daily calculating statistical data (for example, average value, median value) of the haze signal obtained by the evaluation device 100 in the semiconductor manufacturing process for samples (products or semi-finished products) that are judged to be non-defective, rather than measuring the reference sample. The reference haze signal may also be set by simulating the haze signal that can be obtained for each detection optical system Bn based on the design data of the sample W. In other words, actual values or theoretical values can be used as the reference haze signal.

Furthermore, the haze map of the sample W (including the measured reference haze signal) can be created from the haze signal obtained by scanning the sample W during defect inspection in the evaluation device 100. However, since defect inspection generally applies inspection conditions that make it difficult for haze light to occur, it is conceivable that the haze light of the intensity required for evaluating the aging conditions may not be sufficiently detected. In that case, a haze map may be obtained by scanning the sample W under conditions that make it easy for haze light to occur, particularly for the sample W used to evaluate the aging conditions, for example the first and Nth samples W, separately from the defect inspection.

-Correlation data-
Further, in addition to the reference haze signal, the storage device DB prestores a correlation between the detection optical system Bn (in other words, the emission direction of the haze light) and a fluctuation factor of the haze signal.

In the semiconductor manufacturing process, there are several dozen steps in which plasma processing is performed. Each step has different materials (such as the film quality of the sample and the type of gas used in the chamber) and processing conditions. In some steps, the surface roughness of the sample W after etching is likely to change, while in other steps the surface film thickness is likely to change. In other cases, a characteristic tendency may be likely to emerge in accordance with the direction of gas flow in the chamber of the process device. The "parameters that are likely to vary" differ for each step. Therefore, it is desirable for the evaluation device 100 to evaluate the aging conditions using the signal from a detector that is likely to detect changes in the parameters that are likely to vary.

For example, if the surface roughness of the sample W is prone to fluctuate within a certain range, this tends to manifest itself mainly as a change in the intensity of scattered light incident on the openings α3 and α4 located in the direction of specular reflection of the illumination light relative to the beam spot BS. Also, if the surface film thickness of the sample W is prone to fluctuate within a certain range, this tends to manifest itself mainly as a change in the intensity of scattered light incident on the openings α1 and α6 located on the opposite side of the direction of specular reflection relative to the beam spot BS. Based on this relationship, the process-dependent variation factors and the detection optical system that is highly sensitive to the variation factors are associated and stored as correlation data in the storage device DB. A specific detection optical system Bn is selected by the signal processing device D based on this correlation data, and the haze signal of the selected detection optical system Bn for the first sample W of a lot that involves aging processing is monitored, thereby accurately evaluating the aging conditions of the process device.

The above correlation data is merely one example of the correlation between the fluctuation factors of the haze signal and the detection optical system Bn. For example, if it is known that a change in the condition of the process device appears in the haze signal acquired by a detection optical system other than the detection optical system Bn corresponding to the apertures α3, α4, α1, and α6, the correlation data can be set based on that knowledge. Furthermore, the change in the condition of the process device may appear not only in the haze signal detected individually by the detection optical system Bn, but also in the difference or sum of the haze signals detected by multiple detection optical systems Bn. In this case, the correlation data based on the correlation is defined, and the difference or sum of the haze signals detected by multiple detection optical systems Bn can be compared as one form of haze signal with the difference or sum of the reference haze signals related to the same set of detection optical systems Bn, and used to evaluate the aging conditions.

When evaluating the aging conditions, the signal processing device D reads the correlation data from the storage device DB and automatically selects the detection optical system that correlates with the evaluation of the aging conditions of the process device. However, the signal processing device D can also select the detection optical system according to the specification made by the operator via the input device E2. Then, the signal processing device D evaluates the aging conditions of the process device based on the haze signal output from the selected detection optical system. For example, following the example described above, the signal processing device D selects the detection optical system Bn corresponding to the openings α3, α4, α1, and α6 located in the regular reflection direction of the illumination light with respect to the beam spot BS. From the difference between the haze signal detected by the detection optical system Bn corresponding to the openings α3 and α4 and the reference haze signal, a change in the surface roughness of the sample W in a predetermined range, and thus a change in the condition of the process device related to the change in the surface roughness in this predetermined range, is detected. From the difference between the haze signal of the detection optical system Bn corresponding to the openings α1 and α6 and the reference haze signal, a change in the surface film thickness of the sample W in a predetermined range, and thus a change in the condition of the process device related to the change in the surface film thickness in this predetermined range, is detected.

Furthermore, even if the haze light is incident on the same detection optical system Bn, the way in which the condition fluctuation due to imperfect aging conditions appears may differ depending on the polarization direction. In this respect, in this embodiment, the detection optical system Bn is equipped with a polarizing beam splitter Bc that splits the light according to the polarization direction, and multiple sensors Cn, Cn' that detect the light with different polarization directions split by the polarizing beam splitter Bc (FIG. 11). Therefore, in this embodiment, in each detection optical system Bn, two haze signals with different polarization directions can be obtained for the same coordinates on the sample. Therefore, the polarization direction of the haze light can be added as a parameter of the correlation data between the above-mentioned fluctuation factor of the haze signal and the detection optical system Bn, and a more precise relationship between the haze signal and its fluctuation factor can be specified and stored in the storage device DB. In this way, the parameters of the correlation data are increased, and the change in the condition of the process device can be detected more precisely from the haze signal. The aging conditions can be precisely evaluated by accurately detecting the change in the condition of the process device for the first sample W of the lot processed with the aging conditions.

There may also be a correlation between the condition of the process device and the position on the sample W. On the sample W processed in the plasma process device, the haze map can identify areas that have changed significantly compared to the sample used to obtain the reference haze signal (areas where there is a high possibility that some kind of defective workmanship has occurred). If a defect in the aging conditions is detected, attention can be focused on this area on the sample, and the parameters of the aging conditions can be revised so that the difference between the haze signal of this area and the reference haze signal is eliminated. It is also possible to identify the correlation between the haze signal and the parameters, and have the evaluation device 100 automatically calculate the parameter adjustments of the recommended aging conditions according to the difference between the haze signal of the defective area and the reference haze signal, and display them on the monitor E3.

- Determination and operation of aging conditions -
Here, Fig. 13 is a flow chart showing a typical scene for evaluating the aging conditions of process equipment in the process from semiconductor research and development to manufacturing (high volume manufacturing). Among steps S100-S600 shown in Fig. 13, steps S100-S300 are the processes of the semiconductor research and development line, and steps S400-S600 are the processes of the semiconductor manufacturing line.

Step S100
In step S100, the manufacturer of the process equipment extracts candidate conditions that are candidates for the final aging conditions for the process equipment. Even for the same type of process equipment, the aging conditions differ depending on the process used and the type of sample W to be processed. At this stage, process conditions for processing the sample W (e.g., etching, film formation, polishing, etc.) in the process equipment are set according to the planned process and the type of sample W. The aging conditions are set according to the process conditions set in advance. In other words, the aging conditions are set so that when multiple samples W are processed in the target process equipment with the set process conditions at an interval of a predetermined time or more from the first or previous processing, the processing state of the multiple samples W does not vary. Here, multiple candidate conditions are tried to set the aging conditions. As described above, the candidate conditions include basic conditions according to the process in which the process equipment is used and the type of sample W to be processed, as well as multiple variations of conditions in which each parameter of the basic conditions is finely changed. Examples of parameters include microwave and plasma discharge time, gas flow rate, etc. Then, the sample W is processed under predetermined process conditions in the process equipment with aging treatment for each candidate condition, and the processing state of the first sample W is compared with that of a reference sample (e.g., the Nth sample W). As a result, the candidate condition with the least variation in the processing state is set as the aging condition for the target process equipment, assuming that the difference in the processing state is within the allowable value, and is presented to the semiconductor manufacturer. If no aging condition can produce a satisfactory result, further different aging conditions are tried.

Step S200
In the next step S200, the aging conditions presented by the manufacturer of the process equipment are adjusted in the semiconductor manufacturer using the process equipment before being applied to the semiconductor manufacturing line. In this step, the aging conditions are set based on the aging conditions presented by the manufacturer of the process equipment, with adjustments made according to the interval between lots actually flowing through the line (standby time of the process equipment). Then, aging processing and processing of the lot of the sample W are performed under the conditions set in the process equipment according to the standby time of the process equipment, and the processing state of each first sample W and the reference sample are compared as in step S100 to confirm that the difference is within the tolerance. If there is a lot whose difference is not within the tolerance, the aging conditions for the standby time related to that lot are adjusted, and the trial is repeated until the aging conditions whose difference is within the tolerance are set. A lot refers to a set of multiple samples, with N = 2 at a minimum. It is preferable to use fewer samples for condition setting and adjustment.

Step S300
Step S300 is a final confirmation test before applying the aging conditions set in step S200 to the manufacturing line. Each set aging condition is tried on the manufacturing line or on equipment simulating a manufacturing line, and the processing state of the sample W after processing is compared in the same manner as in steps S100 and S200. If it is confirmed that the difference in the processing state between the first sample W and the reference sample is within the allowable value, the aging conditions (set) tried here are determined as the final aging conditions for the target process equipment. If the difference does not fall within the allowable value, the process returns to step S200 and the set of aging conditions is reset.

Step S400
Step S400 is a process in which the aging conditions determined in the research and development line are applied to the semiconductor manufacturing line and operation is started. After this, in the manufacturing line, aging processing is performed in the process equipment as necessary under conditions according to the waiting time. The sample W processed in the process equipment is inspected for defects in-line. Also, as necessary, it is subjected to inspection for the suitability of the aging processing.

Steps S500 and S600
The suitability of the aging conditions applied to the semiconductor manufacturing line is inspected appropriately (e.g., periodically or at appropriate times) during the semiconductor manufacturing process (step S500). If the processing state of the sample W is good, the manufacturing line continues to operate, and if the processing state of the sample W indicates that the aging conditions are insufficient, the aging conditions are adjusted (step S600). When adjusting the aging conditions, the process equipment that performs the adjustment is stopped if necessary, and operation is resumed once the aging conditions have been adjusted.

- Flow of aging condition evaluation -
Here, Fig. 14 is a schematic flow chart showing an example of the flow of aging condition evaluation of a process device. The flow of Fig. 14 is appropriately performed, for example, in the evaluation of aging conditions performed in the semiconductor research and development stage of steps S100-S300 in Fig. 13, and in the evaluation of aging conditions performed in the semiconductor manufacturing stage of steps S500 and S600. Here, a plasma etching device will be described as a specific example of a process device.

In the flow of FIG. 14, first, in the plasma etching device, a lot of samples W is plasma etched with aging processing under the aging conditions to be evaluated (step S10).

In this embodiment, the evaluation device 100, which is also an optical defect inspection device, is used to perform in-line defect inspection of a lot of samples W processed in a plasma etching device. "In-line" means "as one process of semiconductor manufacturing" or "in the course of semiconductor research, development, and manufacturing line." At that time, the signal processing device D (FIG. 12) described above evaluates the validity of the aging conditions applied to the plasma etching device based on the haze signal obtained from at least the first sample W of the lot processed in the plasma etching device (step S21). The haze signal used to evaluate this aging condition may be a haze signal obtained during defect inspection of the sample W, or a haze signal obtained by scanning the sample W under conditions under which haze light is easily detected before or after defect inspection. Then, in the evaluation device 100, the haze signal obtained from the sample W is compared with the reference haze signal, and it is determined whether the difference between the two is within a reference range (step S22). A detailed example of the aging condition evaluation by the evaluation device 100 in steps S21 and S22 will be described later.

If the evaluation device 100 determines in step S22 that the difference is within the reference range, it is presumed that the chamber was in the desired condition when the first sample W was processed, that is, the aging process was necessary and sufficient. In this case, the evaluation device 100 notifies this via monitor E3, and the flow in FIG. 14 ends. For example, when the flow in FIG. 14 is executed on a production line, if no defects in the aging conditions are detected, the production line will continue to operate as is. However, when the flow in FIG. 14 is executed in step S100 of FIG. 13, there is a possibility that other candidates for better aging conditions remain, so the flow in FIG. 14 is then executed for the untried aging conditions.

If the difference is outside the reference range in step S22, it is assumed that the chamber was not in the desired condition when processing the first sample W, i.e., the aging process was insufficient, and an alarm is output from the evaluation device 100 to the monitor E3 (step S30).

An operator or the like who has confirmed the alarm takes appropriate measures for the aging conditions related to the alarm (step S40). An appropriate measure for the situation would be to adjust the defective aging conditions in the processes of steps S200, S300, and S600 in FIG. 13, or to remove the defective aging conditions from the candidates in the process of step S100. When the flow of FIG. 14 is executed on a production line, it may be necessary to stop the plasma etching equipment in step S40. Adjusting the aging conditions may require, in some cases, an analysis of factors that cause fluctuations in the condition of the process equipment. Analysis of factors that cause fluctuations in the condition of the process equipment may involve, for example, cutting out defective areas that have occurred in the sample W using a FIB (Focused Ion Beam) and observing them with a TEM (Transmission Electron Microscope).

--Conventional example--
Here, in FIG. 14, in addition to the evaluation process of the aging conditions according to this embodiment, the evaluation process of the quality of the samples processed by a general process device is shown by dotted lines. As a typical example, after the process of step S10, the CD values of one to several samples of the first and Nth samples W of a lot processed by a plasma etching device are measured by sampling, for example, by OCD (step S26). In addition, the etching rate (surface film thickness) of one to several wafers is also measured by sampling, for example, by a spectroscopic ellipsometer (step S28). The order of measuring the CD values and etching rates can be arbitrarily changed. Then, it is determined whether the difference in the CD values and etching rates of the first and Nth samples W measured initially falls within a reference range (steps S27, S29). If the difference in the measurement results falls outside the reference range, it is suspected that the chamber used to process the first sample W was not in the desired condition, and the procedure moves to step S30. If the difference in the inspection results is within the reference range, it is presumed that the chamber was in the desired condition when the first sample W was processed, and the flow in Fig. 14 ends. As mentioned above, since none of the measuring devices routinely measures all samples in a lot, and sampling measurements are often performed, the first sample measured first is not necessarily the first sample in that lot that was processed in the process device.

In addition, in the CD measurement in step S26, since the measurement target is CD, it is not possible to evaluate the aging conditions using a sample W on which no pattern is formed. Therefore, in order to evaluate and determine the aging conditions on an R&D line, a wafer with a pattern must be prepared for measurement, which is costly. Also, the CD value is only measured on a partial area of the sample W, and it is not possible to check the processing state of the entire surface of the sample W evenly, and there are cases where deficiencies in the aging conditions cannot be detected.

In addition to OCD, CD-SEM (Critical Dimension-Scanning Electron Microscope) is sometimes used as a device to measure CD values. However, the same issues arise when using CD-SEM as when using OCD.

In addition, the measurement of the etching rate in step S28 does not require a pattern to be formed on the sample W, but like the measurement of the CD value, it is performed only on a portion of the sample W, and the measurement area is localized, so there is a possibility that defects will be missed.

The above CD values and etching rates are highly correlated with the shape (processing state) of the sample W and are relatively easy to measure, so they are primarily measured to evaluate the aging conditions of process equipment in semiconductor manufacturing processes. However, in addition to CD value and etching rate inspections, there have long been other inspections that are used to evaluate aging conditions. For example, defect observation using a TEM.

TEM is effective for analyzing aging conditions because it allows detailed observation of defects that occur in the sample W. Although it allows detailed observation, it takes a much longer time to perform measurements than OCD or CD-SEM. In addition, TEM is generally called destructive testing, and test pieces for TEM observation must be cut out of the wafer using, for example, an FIB, and wafers with parts cut out lose their product value and cannot be returned to the line. In addition, it is necessary to take the sample W from the production line to an FIB device, and the cut sample piece must be transported from the FIB device to the TEM, so using a TEM only to evaluate aging conditions takes more time and effort than necessary. Furthermore, because the test piece observed with a TEM is only a tiny portion of the sample W, the evaluation accuracy of the aging conditions is greatly affected by the location of the test piece.

Other process equipment includes an OES (Optical Emission Spectrometer) or a temperature sensor that can collect data on the state inside the chamber during processing. The OES can monitor the plasma light emission state inside the chamber. A temperature sensor can measure the temperature of the sample W or inside the chamber during processing. However, while the OES can observe the plasma light emission state, it cannot divide the state of the entire chamber space into small regions and monitor the state of each region. Furthermore, the temperature measurement of the sample W by the temperature sensor is also local. Therefore, the data collected by the plasma etching equipment is too rough to be sufficient for verifying the correlation between the processing state of the sample W and the aging conditions.

- Impact of improper aging conditions on samples -
Fig. 15 is a schematic diagram showing the change over time in temperature of a chamber of a process device, and Fig. 16 is a schematic diagram showing the effect on a wafer caused by the change over time in temperature of a chamber of a process device. The horizontal axis of Fig. 15 represents time, and the vertical axis represents the temperature in the chamber. Again, a plasma etching device will be used as a specific example of a process device. For convenience, the lot of sample W to be processed in the examples of Figs. 15 and 16 will be referred to as "Lot F".

If the plasma etching apparatus continues to be in a standby (idling) state, the chamber cools down and the chamber temperature falls below the stable temperature at which the sample W can be successfully processed if plasma etching is performed under the set process conditions. When processing a lot F of samples W after a standby state that has continued for a certain period of time, an aging process such as a run-in discharge is performed in the plasma etching apparatus before processing the first sample W in lot F. However, as shown in Figure 15, if processing of the first sample W begins before the chamber temperature has risen to the stable temperature after plasma etching, the chamber may not be in the desired condition when processing the first sample W.

As mentioned above, the change in the conditions in the plasma etching device is reflected in the processing state of the sample W. FIG. 16 shows an example of a haze map (haze distribution on the sample surface) obtained by the evaluation device 100 for the first, second, ..., Nth sample W that has been subjected to plasma etching processing in the temperature environment as shown in FIG. 15. In the examples of FIGS. 15 and 16, when processing the second and subsequent samples W, the chamber temperature reaches a stable temperature and the chamber conditions, including the degree of gas saturation in the chamber, are set. In this case, as shown in FIG. 16, the first sample W that has been processed at a temperature below the stable temperature shows a difference in the intensity of the haze signal at any point, such as the edge (outer edge) X1 or local area X2, compared to the second and subsequent samples W that have been processed after the stable temperature has been reached.

In this embodiment, the aging conditions applied to the aging process of lot F in the plasma etching device are evaluated by the signal processing device D as described above, based on the haze signal obtained by scanning the first sample W of lot F using the evaluation device 100.

In addition, the CD measurement using an OCD or the like, the etching rate measurement using a spectroscopic ellipsometer or the like, the monitoring of the plasma state using an OES, and the localized detailed observation of a sample using a TEM, all of which are compatible with inspection using haze light, as previously explained as conventional examples. It goes without saying that aging conditions can be evaluated using haze light in conjunction with these measurements.

-Detailed procedure for evaluating aging conditions-
FIG. 17 is a flowchart showing the procedure of the evaluation process of the aging condition by the evaluation device 100 according to the present embodiment. The process in the figure is executed in steps S21 and S22 in the flowchart in FIG. 14. Here, the case where the aging condition is evaluated using the lot F (the number of samples W is N) used in the description of FIG. 15 is illustrated. In the following description, the scanning of the lot F is completed at the start of the flow in FIG. 17, and the haze signals of at least the first and Nth samples W are stored in the storage device DB (FIG. 1) are illustrated. Then, the case where the haze signal obtained from the Nth sample W is used as the reference haze signal is illustrated. However, if the reference haze signal already exists at the time of scanning the first sample W, the flow in FIG. 17 can be configured to be executed in parallel with the scanning of the first sample W. In this case, the procedure of step S202 described later is the first procedure of the loop.

When the flow of FIG. 17 starts, the signal processing device D of the evaluation device 100 reads the reference haze signal, that is, the haze signal obtained from the Nth sample W, from the storage device DB (step S201). The signal processing device D also reads the haze signal obtained from the first sample W from the storage device DB (step S202). The order of steps S201 and S202 may be reversed.

Next, the signal processing device D compares the haze signal obtained from the first sample W with the reference haze for an arbitrary region of the sample W (step S203) and determines whether the difference between the two is within a preset value (step S204). The arbitrary region may be the entire surface of the sample W, or may be limited to a specific region within the sample W. Alternatively, the sample W may be divided into a plurality of regions and the comparison may be performed for each region in sequence. If the difference between the haze signal and the reference haze is within the preset value, the signal processing device D records the region as an even region with no significant difference between the haze signal and the reference haze (the difference is within the preset value) (step S205). Conversely, if the difference between the haze signal and the reference haze exceeds the preset value, the signal processing device D records the region as a difference region with a significant difference between the haze signal and the reference haze (the difference exceeds the preset value) (step S206). In this example, the reference sample is the Nth sample W, so the entire surface is not necessarily in a normal processing state, but if we assume that the Nth sample W is formed normally over its entire surface, the difference region is an area where there is a high possibility that some kind of abnormality has occurred.

When the sample W is divided into multiple regions, the signal processing device D repeats the processing of steps S203-S206 for each region, and once processing has been performed for all regions of the sample W, it determines whether the evaluation results are within the standard range, specifically, whether the number of difference regions is equal to or less than a preset tolerance (step S207).

If the difference from the reference haze over the entire surface of the sample W is below a set value or the number of difference areas is below an allowable value, the signal processing device D presumes that there is no problem with the aging conditions of the aging process performed as pre-processing for lot F. In this case, the signal processing device D generates data indicating that there is no problem with the aging condition process device, records it, for example, in the storage device DB, and displays it on the monitor E3 via the control device E1, and ends the procedure (step S208). On the other hand, if the difference from the reference haze exceeds a set value or the number of difference areas exceeds an allowable value, a defect in the aging conditions is suspected. In this case, the signal processing device D generates alarm data indicating that there is a suspected defect in the aging process performed as pre-processing for lot F, records it, for example, in the storage device DB, and outputs it to the monitor E3, and ends the procedure (step S209).

For example, an algorithm may be used that generates a difference image between the haze map of the first sample W and the haze map of the reference haze signal and compares them. It is also possible to employ an algorithm that counts the number of difference regions in the difference image between the haze maps of the sample W and the reference haze signal.

The distribution of the haze map can also be added to the judgment criteria. For example, an algorithm can be applied that divides the surface of the sample W into a circular center portion and an annular outer periphery surrounding it, focuses on changes in the haze signal in a specific portion of the center and outer periphery, for example the outer periphery, and infers that there is some kind of deficiency in the aging conditions if there is a certain level of change in the outer periphery. Other possible judgment conditions could be that there is a certain level of change in the haze signal in both the center and outer periphery, or that there is a certain level of change in the haze signal only in the center or only in the outer periphery.

In addition, the output to monitor E3 in steps S208 and S209 may be an output that does not go through a monitor. In addition, the output format may be a sound or text message, or a haze map for sample W may also be output. For example, when outputting to monitor E3, data on changes in characteristics that appear in sample W (e.g., changes in surface roughness and surface film thickness) may be output as additional information together with an alarm.

-effect-
(1) In this embodiment, the first sample W processed in one lot with aging treatment in the process equipment is optically scanned, and the haze signal obtained from the sample W is compared with the reference haze signal to evaluate the aging condition. The evaluation device 100, which is also a defect inspection device for the sample W, can evaluate the aging condition other than the defect. Since the results can be obtained much faster than the TEM used for detailed observation to adjust the aging condition, the time required for the aging condition evaluation can be significantly reduced. If a haze signal sufficient for evaluating the aging condition is obtained under the scan condition for defect inspection in the evaluation device 100, there is no need to scan the sample W separately for the purpose of evaluating the aging condition in addition to the defect inspection in the evaluation device 100, and a further time-saving effect can be expected.

Also, the entire surface of the sample W can be inspected thoroughly for changes that occur in the sample W in response to fluctuations in the conditions of the process equipment. In particular, since the effects of fluctuations in the conditions of a plasma processing device such as a plasma etching device appear locally within the surface of the sample W, by inspecting the entire surface of the sample W thoroughly, it is possible to ensure high reliability in the evaluation of the aging conditions from the viewpoint of suppressing inspection omissions. Furthermore, by using the distribution of the haze signal over the entire surface of the sample W, that is, the haze map, it is possible to visualize the processing state by the process device and, in turn, the condition inside the chamber of the process device. For example, in the case of a plasma processing device, if the plasma gas phase state, such as gas concentration and radical density, can be grasped based on the haze map, it will lead to smooth setting of not only the aging conditions but also the process conditions. In this respect, it is advantageous compared to devices that measure only the sampling points of the sample, such as OCDs and spectroscopic ellipsometers.

Furthermore, when evaluation of aging conditions using haze light is applied to a semiconductor manufacturing line, the evaluation device 100 can detect deficiencies in the aging conditions of the process equipment in a timely manner in conjunction with the defect inspection of samples W that is performed daily in-line. Since the evaluation results of the aging conditions based on this haze light can be obtained in conjunction with the defect inspection, there is no increase in the workload or inspection costs imposed on the operator. Since deficiencies in the aging conditions of the process equipment are detected early in the defect inspection that is performed daily, the occurrence of defects in samples W is suppressed, and the occurrence of samples W that are subjected to analysis of the cause of defects using a TEM or the like is also suppressed. Furthermore, it is expected that the effects of preventing the occurrence of defects in samples W and improving the operating rate of the process equipment will also be expected, and an improvement in yield can also be expected.

Furthermore, unlike the case where the CD value is measured using an OCD or CD-SEM, the aging conditions can be evaluated even on a sample W on which no pattern is formed. In the semiconductor research and development stage, it is sufficient to prepare a sample W on which no pattern is formed (e.g., a bare wafer) as an evaluation wafer, so there is no need to prepare a patterned sample just for evaluation, which is much more expensive per piece. In addition, since there is no need to go through multiple processes to form a pattern, the aging conditions can be determined in a short period of time. Furthermore, since destructive inspection is not required as in the case of using a TEM, the processing time using FIB, etc. can be reduced. Since the evaluation is performed using a high-speed optical defect inspection device, the measurement time is also short to begin with. By evaluating the aging conditions based on the haze signal, it is also possible to reduce the frequency of CD measurement using an OCD or CD-SEM and inspection using a TEM.

(2) In the evaluation device 100, multiple detection optical systems Bn are arranged with each system facing in a different direction relative to the beam spot BS. This makes it possible to select one or more detection optical systems Bn that are effective in capturing intensity changes in haze light, and to evaluate the aging conditions using only the selected ones of the multiple detection optical systems Bn. If the signals of multiple detection optical systems Bn were merged and output regardless of their sensitivity to haze light, the changes detected with high sensitivity by a specific detection optical system Bn would be diluted, and the inspection sensitivity would decrease. In contrast, in this embodiment, a configuration with multiple detection optical systems Bn facing different directions is used to perform aging condition evaluation with high sensitivity.

In this embodiment, in particular, the correlation between the haze signal and its fluctuation factor for each detection optical system Bn is stored in the storage device DB, and the detection optical system Bn with a specific azimuth angle φ1 can be selectively used for aging condition evaluation based on this correlation. This makes it possible to evaluate the aging condition according to the fluctuation factor of the haze light intensity, such as the surface film thickness and surface roughness of the sample surface. In this embodiment, an example has been described in which a change in the condition of a process device related to a change in a specified range of the surface roughness of the sample W is detected from the difference between the haze signal incident on the openings α3 and α4 located in the direction of specular reflection of the illumination light with respect to the beam spot BS and the reference haze signal. Also, an example has been described in which a change in the condition of a process device related to a change in a specified range of the surface film thickness of the sample W is detected from the difference between the haze signal incident on the openings α1 and α6 located in the opposite direction to the specular reflection of the illumination light with respect to the beam spot BS and the reference haze signal.

Furthermore, in this embodiment, the detection optical system Bn separates the haze light according to its polarization direction using the polarizing beam splitter Bc, and can detect two lights with different polarization directions for the haze light emitted in the same direction from the same coordinates on the sample W. With this configuration, it is possible to perform a more precise aging condition evaluation that includes the polarization direction of the haze light as a parameter, based on correlation data that specifies the intensity, polarization direction, and fluctuation factors of the haze signal for each detection optical system Bn, i.e., for each emission direction of the haze light.

Second Example
In the first embodiment, an example was described in which the haze signal of each region of the sample W was compared with the reference haze signal to evaluate the aging condition. In a semiconductor manufacturing line, if data related to the evaluation performed daily in this manner is accumulated and machine learning is performed, the aging condition can also be evaluated based on the learned model.

The trained model is an inference program in which trained parameters are incorporated through machine learning of training data, and outputs evaluation results of aging conditions for input data related to a haze signal. This trained model is created by the signal processing device D or the control device E1, and is stored, for example, in the storage device DB. The signal processing device D uses this trained model to evaluate the aging conditions based on the haze signal acquired during defect inspection of a sample W that has been processed with an aging process.

An example of learning data is actual data accumulated in the daily semiconductor manufacturing process, such as the haze map of sample W, the polarization direction of haze light, the standby time of the process equipment, the evaluation results of the aging conditions, the adjustment history of the aging conditions, and the positive or negative evaluation results. The standby time of the process equipment is, for example, received from the process equipment or data accumulated in the storage device DB by input by an operator, etc. The adjustment history of the aging conditions and the positive or negative evaluation results are a type of feedback data, and can be input by, for example, a person who adjusted the aging conditions using the input device E2 according to a previously prepared input screen. The positive or negative evaluation results are, for example, the judgment of the person who adjusted the aging conditions, and are matters such as whether or not the alarm notified from the evaluation device 100 was truly a notification of a deficiency in the aging conditions.

FIG. 18 is a conceptual diagram of machine learning. Here, an example will be described in which machine learning is performed in the signal processing device D and a trained model is generated. The signal processing device D searches for and reads from the storage device DB performance data such as the haze map of the sample W described above, waiting time of the process device, evaluation results of aging conditions, and adjustment history, to generate training data. The signal processing device D loads this training data into the neural network D9, and optimizes the weighting of the connections between neurons in the input layer, intermediate layer, and output layer. As a result, a trained model for evaluating the aging conditions is generated from the haze signal data obtained for the sample W, such as the scattering direction, light intensity, polarization direction, and coordinates. Note that the trained model is not limited to being generated by the signal processing device D, and may be generated by another computer.

It is also possible to use the signal processing device D or the control device E1 to perform machine learning using the haze map and the adjustment history of the aging conditions as input, identify correlations with the haze map for each parameter of the aging conditions, and present adjustment suggestions for the aging conditions.

In other respects, this embodiment is similar to the first embodiment. In this embodiment, as evaluation data based on a comparison of the difference between the haze signal and the reference haze signal is accumulated, a trained model is generated that also takes into account feedback data such as the adjustment history of the aging conditions and the pros and cons of the evaluation results, and it is expected that the evaluation accuracy of the aging conditions will be improved. In addition, in situations where the aging conditions need to be adjusted as described above, it is expected that the evaluation device 100 will support the operator in adjusting the conditions by suggesting adjustments.

(Third Example)
Fig. 19 is a schematic diagram of the essential parts of an aging condition evaluation device according to one modification of the present invention. In Fig. 19, elements that are the same as or correspond to those described in the first and second embodiments are given the same reference numerals as in the previously mentioned drawings, and description thereof will be omitted.

This embodiment is an example in which data obtained by multiple evaluation devices is included in the basic data of the reference haze signal (first embodiment) or the trained model (second embodiment) described above. In this embodiment, the evaluation device 100 is connected to a data server DS via a network (not shown) as appropriate. Other evaluation devices 100' and 100", different from the evaluation device 100, are connected to this data server DS via a network as appropriate. The

evaluation devices

100, 100', and 100" are preferably of the same type or similar types (same series, same manufacturer, etc.), but may be devices of different types. Although two other evaluation devices 100' and 100" are illustrated in FIG. 19, the number of other evaluation devices connected to the data server DS may be one or three or more.

Evaluation data and the like are input to the data server DS from the

evaluation devices

100, 100', 100", and this data is stored. This stored data can include, for example, evaluation data on aging conditions including haze signals and evaluation results for each evaluation device, as well as design data for the sample W, adjustment history of aging conditions, the pros and cons of the evaluation results, and inspection data for the sample W. In addition, with regard to defect inspection of the sample W, inspection conditions (inspection recipe), defect review data, defective material analysis data, and the like can also be stored in the data server DS. Defective material analysis data is, for example, information obtained by energy dispersive X-ray analysis. This may be a stand-alone device. However, these may be installed in the defect review device and can be acquired together with the defect review information. In the data server DS, a reference haze signal and a trained model for evaluating the aging conditions are calculated based on this accumulated data. The calculation of the reference haze signal and the trained model can be performed in the data server DS at regular intervals, or can be performed when a certain amount of new data is accumulated. Each

evaluation device

100, 100', 100" receives the latest reference haze signal or trained model from the data server DS at the opportunity to evaluate the aging conditions and performs an evaluation of the aging conditions.

According to this embodiment, in addition to the evaluation device 100's own data, a reference haze signal or a learned model is calculated using a large amount of data from the other evaluation devices 100, 100' as basic data. Therefore, a large amount of basic data is accumulated in a short period of time, and the evaluation accuracy of the aging conditions can be improved over time.

(Fourth Example)
Fig. 20 is a schematic diagram for explaining the main functions of an aging condition evaluation device according to a fourth embodiment of the present invention. In Fig. 20, elements that are the same as or correspond to those explained in the first to third embodiments are given the same reference numerals as in the previously mentioned drawings, and explanations thereof will be omitted.

This embodiment is a variation of the method of acquiring a haze signal. A sample transfer position Pa and an inspection start position Pb are set on the movement axis of the translation stage of the stage ST, and by driving the translation stage, the stage ST moves along a straight line passing through these positions. The inspection start position Pb is the position where the sample W is irradiated with illumination light to start inspection of the sample W, and is the position where the center of the sample W coincides with the beam spot BS of the illumination optical system A. The sample transfer position Pa is the position where the sample W is attached to and detached (loaded and unloaded) from the stage ST by the arm Am, and the stage ST, having received the sample W, moves from the sample transfer position Pa to the inspection start position Pb.

In response to recent demands for even higher sensitivity inspections, the detection optical system Bn is positioned close to the sample W. When the stage ST is directly below the detection optical system Bn, the gap G between the stage ST and the detection optical system Bn is about a few mm or less. Because it is difficult to insert the sample W into the gap G with the arm Am at the inspection start position Pb and place it on the stage ST, a configuration is adopted in which the sample W is transferred at a sample transfer position Pa away from the inspection start position Pb.

In defect inspection, the sample W is generally scanned with P-polarized illumination light while the stage ST moves from the inspection start position Pb, but in this embodiment, a preliminary scan is performed while the stage ST moves from the sample transfer position Pa to the inspection start position Pb. In the preliminary scan, the illumination light is set to S-polarized light, and the sample W is scanned in a spiral trajectory from the outer periphery toward the center. Then, an evaluation process of the aging conditions is performed based on the haze signal obtained in this preliminary scan.

In other respects, this embodiment is similar to the first, second, or third embodiment.

Here, in the defect inspection of sample W, the inspection conditions are set so as to suppress the generation of haze light, which generally becomes noise in defect inspection (for example, the illumination light is set to P-polarized light). Therefore, depending on other conditions, it may be possible that the defect inspection of sample W cannot adequately detect haze light, making it difficult to evaluate the aging conditions based on the haze signal.

In contrast, in this embodiment, the sample W can be moved from the sample transfer position Pa to the inspection start position Pb, allowing a preliminary inspection to be performed under conditions different from those for the defect inspection to collect a haze signal. By using the transport operation of the sample W to collect a haze signal in this way, it is possible to achieve both defect inspection and aging condition evaluation without changing the series of machine operations during defect inspection.

In this embodiment, an example has been described in which a haze signal is acquired when the sample W moves from the sample transfer position Pa to the inspection start position Pb before defect inspection, but a configuration is also conceivable in which a haze signal is acquired when the sample W moves to the sample transfer position Pa after defect inspection.

(Modification)
For example, when scanning the sample W by rotating it as shown in FIG. 2, it is assumed that the intensity of the haze light changes depending on the rotation angle of the sample W even if the detection optical system Bn is the same. In this case, the selection of the detection optical system Bn in the aging condition evaluation for the same sample W can be configured to be switched periodically according to the rotation angle of the sample W. For example, when a patterned wafer is rotated and scanned as the sample W, the scattering direction of the haze light may change regularly due to the influence of diffraction occurring in a fine linear pattern formed periodically vertically and horizontally. In such a case, it may be effective to predefine the relationship data between the rotation angle of the sample W and the selection of the detection optical system Bn and store it in, for example, a storage device DB, and to switch the selection of the detection optical system Bn in the aging condition evaluation according to the rotation angle of the sample W.

In addition to the haze signal, it is also possible to analyze or machine-learn the correlation between the aging conditions, etc. and the haze signal and defect signal data set for the same sample W, and evaluate the aging conditions based on the haze signal and defect signal. It is possible that a defect will occur in the first sample W due to improper aging conditions, or that the defect will affect the haze light, and the accuracy of the aging condition evaluation may be improved by monitoring the defect signal along with the haze signal.

As mentioned above, the plasma etching apparatus may be equipped with an OES for monitoring the plasma discharge state. It is also possible to analyze or learn machine learning the monitor data during plasma etching by this OES together with the haze signal in a signal processing device D or server. If it is possible to identify the correlation between the monitor data of the plasma discharge state and the haze signal, it is expected that the accuracy of the aging condition evaluation can be further improved.

　Furthermore, defect inspection is performed after one or several steps in the semiconductor manufacturing process, and haze signals can be obtained during defect inspection before and after the process by the process equipment being diagnosed. It is also possible to calculate the difference in haze signals obtained by inspection before and after the process for the same sample W, and use this difference to evaluate the degree of processing by the process equipment. In other words, calculating the difference in haze signals obtained by inspection before and after the process for a reference sample as a reference haze signal related to the degree of processing by the process equipment, and comparing a similar difference related to sample W with the reference haze signal can be considered as one form of aging condition evaluation.

Also, in the fourth embodiment, an example was described in which the sample W is scanned under conditions in which haze light is likely to occur and a haze signal is acquired separately from the defect inspection. In this case, haze light is also acquired during the defect inspection, and a new correlation between the haze signal and aging conditions can be identified by comparing and analyzing the difference between the haze signals sampled under conditions in which haze light is likely to occur and conditions in which it is unlikely to occur.

In addition, in each embodiment, as described above, an example has been described in which the haze signals incident on the openings α3, α4, α1, and α6 are used to evaluate the aging condition. However, haze signals incident on other openings can also be used to evaluate the aging condition. For example, a correlation with the aging condition can be found for the sum or difference signal of the haze signals incident on the openings α2, α5, β2, β3, β5, and β6 located to the left and right of the beam spot BS and the haze signals incident on the openings α3, α4, α1, and α6.

100...aging condition evaluation device, A...illumination optical system, Bc...polarized beam splitter, Bn (n = 1, 2...)...detection optical system, BS...beam spot, Cn (n = 1, 2...)...sensor, D...signal processing device, DB...storage device, ST1...sample stage, ST2...scanning device, W...sample, F...lot

Claims

An aging condition evaluation device that evaluates aging conditions, which are setting conditions for an aging treatment of a process device, comprising:
a sample stage for supporting a sample processed in the process device;
an illumination optical system that irradiates illumination light onto the sample placed on the sample stage;
a plurality of detection optical systems that collect light from the sample, convert it into an electrical signal, and output a detection signal;
a signal processing device that processes detection signals of the plurality of detection optical systems,
The signal processing device includes:
Scanning an initial sample that is processed early among a batch of samples processed with an aging treatment in the process device to extract a haze signal of the initial sample;
An aging condition evaluation device, characterized in that the suitability of the aging conditions is judged based on a difference when the haze signal of the initial sample is compared with a reference haze signal.
2. The aging condition evaluation device according to claim 1,
The aging condition evaluation device is characterized in that the signal processing device compares the haze signal of the initial sample stored in the memory device with a reference haze signal based on a haze signal extracted from a sample processed a predetermined time after the initial sample was processed in the process device.
3. The aging condition evaluation device according to claim 2,
the signal processing device processes, among the detection signals of the detection optical system, a signal having a frequency greater than a predetermined value as a defect signal, and a signal having a frequency smaller than the predetermined value as a haze signal;
An aging condition evaluation device which outputs both the defect inspection results and the suitability of the aging conditions for the samples in one lot.
2. The aging condition evaluation device according to claim 1,
The reference haze signal is an actual measurement value obtained by scanning the last processed sample or the latter processed sample among the samples in one lot, or an actual value or a theoretical value.
2. The aging condition evaluation device according to claim 1,
The signal processing device is an aging condition evaluation device that detects a change in the condition of the process device, which appears as a microscopic surface shape of the initial sample, based on a difference between the haze signal and the reference haze signal.
2. The aging condition evaluation device according to claim 1,
The aging condition evaluation apparatus, wherein the plurality of detection optical systems are arranged so that their azimuth angles with respect to the beam spot of the illumination light are different.
7. The aging condition evaluation device according to claim 6,
The signal processing device evaluates the suitability of aging conditions based on the haze signal of a detection optical system among the plurality of detection optical systems that has a stronger correlation with the variation factor, based on correlation data regarding a plurality of detection optical systems and variation factors of a haze signal that is pre-stored in a storage device.
2. The aging condition evaluation device according to claim 1,
The signal processing device includes:
creating a haze map, which is an intensity distribution of the haze signal, for the sample;
An aging condition evaluation device that compares the haze map with a haze map that is an intensity distribution of the reference haze signal, and evaluates the aging condition based on whether or not a difference between the haze signal and the reference haze signal exceeds a set value.
2. The aging condition evaluation device according to claim 1,
The signal processing device is an aging condition evaluation device that accumulates data regarding the evaluation of the aging conditions, performs machine learning, and evaluates the aging conditions based on a trained model obtained by machine learning.
A method for setting aging conditions for adjusting a chamber of a semiconductor processing device to a predetermined condition, comprising the steps of:
subjecting the chamber to a first aging condition and placing a sample in the chamber;
sequentially processing a plurality of samples in said chamber;
Similarly, a plurality of samples are sequentially processed under second aging conditions having parameters different from the first aging conditions, and the samples are measured by an aging condition evaluation device to obtain a plurality of haze signals;
The aging condition setting method selects the second aging condition when a difference between a haze signal obtained from the sample and a reference haze signal is smaller under the second aging condition than under the first aging condition.