Heat treatment method, heat treatment system, and heat treatment apparatus

Abstract

To provide a heat treatment method, a heat treatment system, and a heat treatment device with which it is possible to predict the temperature distribution occurring to substrates easily with high accuracy.SOLUTION: A first trained regression model 150 is constructed by machine learning adopting a condition regarding light irradiation as an input variable and adopting the radiant illumination distribution calculated by an optical simulation as an output variable. A second trained regression model 160 is constructed by machine learning adopting a processing condition that includes the radiant illumination distribution as an input variable and adopting the measured temperature distribution of wafers for monitoring as an output variable. The first trained regression model 150 created based on the optical simulation and the second trained regression model 160 created based on the measurement are combined to derive a composite function 120. A temperature distribution occurring to semiconductor wafers W at light irradiated heat treatment time is predicted on the basis of the composition function 120.SELECTED DRAWING: Figure 13

Description

[Technical Field]

[0001] The present invention relates to a heat treatment method, a heat treatment system, and a heat treatment apparatus for heating a substrate by irradiating the substrate with light, for example, a semiconductor wafer, a substrate for a liquid crystal display device, a substrate for a flat panel display (FPD), a substrate for an optical disk, a substrate for a magnetic disk, or a substrate for a solar cell. [Background technology]

[0002] Flash lamp annealing (FLA), which heats semiconductor wafers in an extremely short time, is attracting attention in the semiconductor device manufacturing process.Flash lamp annealing is a heat treatment technology that uses a xenon flash lamp (hereinafter, simply referred to as "flash lamp" means a xenon flash lamp) to irradiate the surface of a semiconductor wafer with flash light, thereby raising the temperature of only the surface of the semiconductor wafer in an extremely short time (a few milliseconds or less).

[0003] The spectral distribution of radiation from a xenon flash lamp is in the ultraviolet to near-infrared range, with a shorter wavelength than conventional halogen lamps and a wavelength that roughly matches the fundamental absorption band of silicon semiconductor wafers. Therefore, when a semiconductor wafer is irradiated with flash light from a xenon flash lamp, little light is transmitted, making it possible to rapidly heat the semiconductor wafer. It has also been found that if the flash light is irradiated for an extremely short period of time, less than a few milliseconds, it is possible to selectively heat only the area near the surface of the semiconductor wafer.

[0004] Flash lamp annealing is used in processes that require heating for an extremely short period of time, such as activating impurities implanted in a semiconductor wafer. By irradiating the surface of a semiconductor wafer into which impurities have been implanted by ion implantation with a flash light from a flash lamp, the surface of the semiconductor wafer can be heated to the activation temperature in an extremely short period of time, allowing only the impurities to be activated without diffusing them deeply.

[0005] Wafer temperature control is important not only in flash lamp annealing but also in any other heat treatment of semiconductor wafers. Because semiconductor wafers are thin substrates, the temperature distribution across their surfaces can become uneven during heat treatment. For this reason, it is necessary to determine the temperature distribution of semiconductor wafers during heat treatment. Patent Document 1 discloses a method for determining the temperature distribution of semiconductor wafers during heat treatment in real time by providing multiple thermometers. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2000-188258 Summary of the Invention [Problem to be solved by the invention]

[0007] However, to obtain the detailed temperature distribution of a semiconductor wafer, it is necessary to install multiple thermometers and perform multi-point measurements, but it is difficult to install such a large number of thermometers in an actual lamp annealing apparatus.The temperature distribution of a semiconductor wafer in a lamp annealing apparatus is mainly determined by the illuminance distribution of light irradiated on the wafer surface and the energy balance due to heat transfer factors such as heat conduction, convection, and radiation.Therefore, by defining a regression equation with these parameters as input variables and the temperature distribution as the output variable, it is possible to predict the temperature distribution of a semiconductor wafer during heat treatment without installing multiple thermometers.

[0008] For this reason, attempts have been made to determine a regression equation by irradiating a monitor wafer with light under various conditions and measuring the temperature distribution on the monitor wafer. The irradiance distribution of light, which is an important input variable when determining the regression equation, is primarily determined by the power input to the lamps installed in the equipment. Typically, a flash lamp annealing equipment is equipped with dozens of lamps. In order to acquire a large amount of data by changing the individual power input settings to the dozens of lamps, it was necessary to use a huge number of monitor wafers.

[0009] The monitor wafers used to measure temperature distribution are doped with impurities, which are activated by light irradiation and heating, causing irreversible reactions in the monitor wafers during heat treatment. As a result, once-heated monitor wafers cannot be reused, and a huge number of monitor wafers are consumed to obtain the necessary data. In other words, when formulating the regression equation, the long processing time required for repeated pre-irradiation treatments and the increased costs associated with consuming a large number of monitor wafers were problematic.

[0010] Furthermore, because it is difficult to obtain a large amount of data, it is difficult to determine a regression equation that expresses the nonlinearity of the temperature distribution, and so a regression equation using multiple linear regression has been unavoidable. As a result, when using this regression equation, the accuracy of temperature prediction is low, especially at the peripheral part of the semiconductor wafer where nonlinearity is most pronounced.

[0011] The present invention has been made in view of the above-mentioned problems, and aims to provide a heat treatment method, a heat treatment system, and a heat treatment apparatus that can predict the temperature distribution occurring in a substrate easily and with high accuracy. [Means for solving the problem]

[0012] In order to solve the above problem, the invention of claim 1 is a heat treatment method for heating a substrate by irradiating the substrate with light, comprising: an irradiance distribution calculation step of calculating an irradiance distribution on the substrate by optical simulation based on conditions related to light irradiation from a lamp; a first learning step of constructing a first trained model by machine learning using the conditions related to light irradiation as input variables and the irradiance distribution calculated by the optical simulation as output variables; a temperature distribution measurement step of measuring a temperature distribution generated on a monitor substrate when the monitor substrate is irradiated with light from the lamp; The method includes a second learning process for constructing a second trained model by machine learning using processing conditions in the temperature distribution measurement process, including the fabric, as input variables and the temperature distribution measured in the temperature distribution measurement process as output variables; a combination process for deriving a composite function by combining the first trained model and the second trained model by transferring the irradiance distribution output from the first trained model as part of the input variables of the second trained model; and a temperature distribution prediction process for predicting the temperature distribution that will occur on the substrate to be processed when light is irradiated from the lamp to the substrate to be processed, based on the composite function.

[0013] In addition, the invention of claim 2 is the heat treatment method according to claim 1, wherein the output of the lamps is controlled based on the predicted temperature value predicted in the temperature distribution prediction step.

[0014] Furthermore, the invention of claim 3 is a heat treatment method according to the invention of claim 1, in which the difference between a target temperature distribution for the substrate to be treated and the temperature distribution predicted in the temperature distribution prediction step is used as an evaluation function, and processing conditions for the substrate to be treated are determined so as to minimize the evaluation function.

[0015] The invention of claim 4 relates to a heat treatment system that heats a substrate by irradiating the substrate with light, the system comprising: an optical simulator that calculates an irradiance distribution on the substrate by optical simulation based on conditions related to light irradiation from a lamp; a first learning device that constructs a first trained model by machine learning using the conditions related to light irradiation as input variables and the irradiance distribution calculated by the optical simulation as output variables; a temperature distribution measuring device that measures the temperature distribution that occurs on a monitor substrate when light is irradiated from the lamp in a heat treatment device; and a second learning device that constructs a second trained model by machine learning using processing conditions during light irradiation of the monitor substrate, including the irradiance distribution, as input variables and the temperature distribution measured by the temperature distribution measuring device as output variables, and the system predicts the temperature distribution that occurs on the substrate to be treated when light is irradiated from the lamp in the heat treatment device based on a composite function derived by combining the first trained model and the second trained model, and passing the irradiance distribution output from the first trained model as part of the input variables of the second trained model.

[0016] The invention of claim 5 is a heat treatment system according to the invention of claim 4, wherein the output of the lamps is controlled based on a predicted value of a temperature that is predicted to occur in the substrate to be treated.

[0017] Furthermore, the invention of claim 6 is a heat treatment system according to the invention of claim 4, in which the difference between the target temperature distribution for the substrate to be treated and the temperature distribution predicted to occur in the substrate to be treated is used as an evaluation function, and processing conditions for the substrate to be treated are determined so as to minimize the evaluation function.

[0018] The invention of claim 7 relates to a heat treatment apparatus for heating a substrate by irradiating the substrate with light, the heat treatment apparatus comprising: a chamber for accommodating a substrate; a holder for holding the substrate in the chamber; a lamp for irradiating light onto the substrate held in the holder; and a control unit for controlling the output of the lamp. The control unit predicts the temperature distribution that occurs on the substrate to be treated in the chamber when light is irradiated from the lamp based on a composite function derived by combining a first trained model constructed by machine learning with conditions related to light irradiation from the lamp as input variables and an irradiance distribution calculated by optical simulation based on the conditions as output variables, and a second trained model constructed by machine learning with processing conditions when light is irradiated from the lamp onto the monitor substrate in the chamber as input variables and a temperature distribution that occurs on the monitor substrate as output variables.

[0019] Furthermore, the invention of claim 8 relates to the heat treatment device of the invention of claim 7, wherein the composite function is derived by combining the first trained model and the second trained model by transferring the irradiance distribution output from the first trained model as part of the input variables of the second trained model.

[0020] The invention of claim 9 provides the heat treatment apparatus according to claim 7, wherein the control unit controls the output of the lamps based on a predicted value of a temperature predicted to occur in the substrate to be treated.

[0021] Furthermore, the invention of claim 10 is a heat treatment apparatus according to the invention of claim 7, wherein the control unit uses the difference between a target temperature distribution for the substrate to be processed and a temperature distribution predicted to occur in the substrate to be processed as an evaluation function, and determines processing conditions for the substrate to be processed so that the evaluation function is minimized. [Effects of the Invention]

[0022] According to the inventions of claims 1 to 3, a composite function is derived by combining a first trained model based on optical simulation with a second trained model based on actual measurements using a monitor substrate, and the temperature distribution occurring on the substrate to be processed is predicted based on this composite function, thereby reducing the consumption of monitor substrates and making it possible to easily and accurately predict the temperature distribution occurring on the substrate.

[0023] According to the inventions of claims 4 to 6, the temperature distribution occurring in the substrate to be processed is predicted based on a composite function derived by combining a first trained model based on optical simulation and a second trained model based on actual measurements using a monitor substrate, thereby reducing the consumption of monitor substrates and making it possible to easily and accurately predict the temperature distribution occurring in the substrate.

[0024] According to the inventions of claims 7 to 10, the temperature distribution occurring in the substrate to be processed is predicted based on a composite function derived by combining a first trained model based on optical simulation and a second trained model based on actual measurements using a monitor substrate, thereby reducing the consumption of monitor substrates and making it possible to easily and accurately predict the temperature distribution occurring in the substrate. [Brief explanation of the drawings]

[0025] [Figure 1] 1 is a diagram showing an example of the configuration of a heat treatment system according to the present invention; [Figure 2] FIG. 2 is a vertical cross-sectional view showing the configuration of a heat treatment apparatus. [Figure 3] FIG. 2 is a perspective view showing the overall appearance of the holding portion. [Figure 4] FIG. [Figure 5] FIG. 2 is a cross-sectional view of a susceptor. [Figure 6] FIG. [Figure 7] FIG. [Figure 8] FIG. 2 is a plan view showing the arrangement of a plurality of halogen lamps. [Figure 9]FIG. 2 is a block diagram showing the configuration of a control unit. [Figure 10] 1 is a flowchart showing a procedure for constructing a first trained regression model. [Figure 11] 10 is a flowchart showing the procedure for constructing a second trained regression model. [Figure 12] 1 is a flowchart showing a procedure for combining and using a first trained regression model and a second trained regression model. [Figure 13] FIG. 2 is a diagram for conceptually explaining the overall processing content in the heat treatment system. DETAILED DESCRIPTION OF THE INVENTION

[0026] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Hereinafter, expressions indicating relative or absolute positional relationships (e.g., "in one direction," "along one direction," "parallel," "orthogonal," "center," "concentric," "coaxial," etc.) not only strictly represent the positional relationship but also represent a state of relative angular or distance displacement within a tolerance or a range that provides equivalent functionality, unless otherwise specified. Furthermore, expressions indicating an equal state (e.g., "identical," "equal," "homogeneous," etc.) not only represent a state of strict quantitative equality but also represent a state of difference that provides a tolerance or equivalent functionality, unless otherwise specified. Furthermore, expressions indicating a shape (e.g., "circular," "square," "cylindrical," etc.) not only represent a geometrically strict shape but also represent a shape within a range that provides equivalent functionality, such as irregularities or chamfers, unless otherwise specified. Furthermore, expressions such as "comprise," "comprise," "include," "have," etc., regarding components, are not exclusive expressions that exclude the presence of other components. Furthermore, the expression "at least one of A, B, and C" includes "A only," "B only," "C only," "any two of A, B, and C," and "all of A, B, and C."

[0027] Fig. 1 is a diagram showing an example of the configuration of a heat treatment system 100 according to the present invention. The heat treatment system 100 includes a heat treatment apparatus 1, an optical simulator 101, a first regression learner 102, a substrate measuring instrument 103, a second regression learner 104, and a multi-objective optimizer 105. Note that in Fig. 1 and the subsequent figures, the dimensions and number of each part are exaggerated or simplified as necessary for ease of understanding.

[0028] The heat treatment apparatus 1 irradiates a semiconductor wafer W with light to heat the semiconductor wafer W. An optical simulator 101 executes optical simulation software to determine the irradiance distribution on the surface of the semiconductor wafer W. A first regression learner 102 constructs a first trained regression model by machine learning to obtain the irradiance distribution on the surface of the semiconductor wafer W. A substrate measuring instrument 103 measures the surface resistance (sheet resistance) of a monitor wafer after the light irradiation heat treatment. A second regression learner 104 constructs a second trained regression model by machine learning to obtain the temperature distribution on the surface of the semiconductor wafer W. A multi-objective optimizer 105 performs multi-objective optimization to determine the processing conditions for the semiconductor wafer W to be processed. Details of each of these elements will be described later.

[0029] Of the elements constituting the heat treatment system 100, the optical simulator 101, the first regression learner 102, the second regression learner 104, and the multi-objective optimizer 105 are each realized by running predetermined software on a general-purpose computer.

[0030] The heat treatment apparatus 1, optical simulator 101, first regression learner 102, substrate measuring instrument 103, second regression learner 104, and multi-objective optimizer 105 are connected to each other online to enable data exchange. Alternatively, a so-called cloud system may be constructed in which data is stored on a single server and used by the heat treatment apparatus 1, optical simulator 101, first regression learner 102, substrate measuring instrument 103, second regression learner 104, and multi-objective optimizer 105. Alternatively, at least one of the heat treatment apparatus 1, optical simulator 101, first regression learner 102, substrate measuring instrument 103, second regression learner 104, and multi-objective optimizer 105 may be offline, and data may be exchanged via a recording medium.

[0031] Fig. 2 is a vertical cross-sectional view showing the configuration of the heat treatment apparatus 1. The heat treatment apparatus 1 in Fig. 2 is a flash lamp annealing apparatus that heats a disk-shaped semiconductor wafer W as a substrate by irradiating the semiconductor wafer W with flash light. The size of the semiconductor wafer W to be treated is not particularly limited, but is, for example, φ300 mm or φ450 mm (φ300 mm in this embodiment).

[0032] Heat treatment apparatus 1 includes a chamber 6 that accommodates a semiconductor wafer W, a flash heating unit 5 that incorporates multiple flash lamps FL, and a halogen heating unit 4 that incorporates multiple halogen lamps HL. The flash heating unit 5 is provided above chamber 6, and the halogen heating unit 4 is provided below. Heat treatment apparatus 1 also includes, inside chamber 6, a holder 7 that holds the semiconductor wafer W in a horizontal position, and a transfer mechanism 10 that transfers the semiconductor wafer W between the holder 7 and the outside of the apparatus. Heat treatment apparatus 1 also includes a control unit 3 that controls the operating mechanisms provided in the halogen heating unit 4, flash heating unit 5, and chamber 6 to perform heat treatment on the semiconductor wafer W.

[0033] The chamber 6 is constructed by attaching quartz chamber windows to the top and bottom of a cylindrical chamber side portion 61. The chamber side portion 61 has a roughly cylindrical shape with openings at the top and bottom, with an upper chamber window 63 attached to and closing the upper opening, and a lower chamber window 64 attached to and closing the lower opening. The upper chamber window 63, which forms the ceiling of the chamber 6, is a disc-shaped member made of quartz and functions as a quartz window that transmits the flash light emitted from the flash heating unit 5 into the chamber 6. The lower chamber window 64, which forms the floor of the chamber 6, is also a disc-shaped member made of quartz and functions as a quartz window that transmits the light from the halogen heating unit 4 into the chamber 6.

[0034] Furthermore, a reflective ring 68 is attached to the upper part of the inner wall surface of the chamber side 61, and a reflective ring 69 is attached to the lower part. Both reflective rings 68, 69 are formed in an annular shape. The upper reflective ring 68 is attached by fitting it from the upper side of the chamber side 61. On the other hand, the lower reflective ring 69 is attached by fitting it from the lower side of the chamber side 61 and fastening it with screws (not shown). In other words, both reflective rings 68, 69 are detachably attached to the chamber side 61. The internal space of the chamber 6, i.e., the space surrounded by the upper chamber window 63, the lower chamber window 64, the chamber side 61, and the reflective rings 68, 69, is defined as a heat treatment space 65.

[0035] By attaching the reflecting rings 68, 69 to the chamber side portion 61, a recess 62 is formed on the inner wall surface of the chamber 6. That is, the recess 62 is formed by a central portion of the inner wall surface of the chamber side portion 61 where the reflecting rings 68, 69 are not attached, the lower end surface of the reflecting ring 68, and the upper end surface of the reflecting ring 69. The recess 62 is formed in an annular shape along the horizontal direction on the inner wall surface of the chamber 6, and surrounds the holder 7 that holds the semiconductor wafer W. The chamber side portion 61 and the reflecting rings 68, 69 are made of a metal material (e.g., stainless steel) that has excellent strength and heat resistance.

[0036] Furthermore, a transfer opening (furnace port) 66 is formed in the chamber side portion 61, through which a semiconductor wafer W is loaded into and unloaded from the chamber 6. The transfer opening 66 can be opened and closed by a gate valve 185. The transfer opening 66 is connected to the outer peripheral surface of the recessed portion 62. Therefore, when the gate valve 185 opens the transfer opening 66, the semiconductor wafer W can be loaded into and unloaded from the heat treatment space 65 through the transfer opening 66 and the recessed portion 62. Furthermore, when the gate valve 185 closes the transfer opening 66, the heat treatment space 65 in the chamber 6 becomes an airtight space.

[0037] Furthermore, through holes 61a and 61b are formed in the chamber side portion 61. The through hole 61a is a cylindrical hole for guiding infrared light radiated from the upper surface of a semiconductor wafer W held on a susceptor 74 (described later) to the infrared sensor 29 of the upper radiation thermometer 25. On the other hand, the through hole 61b is a cylindrical hole for guiding infrared light radiated from the lower surface of the semiconductor wafer W to the infrared sensor 24 of the lower radiation thermometer 20. The through holes 61a and 61b are provided at an angle with respect to the horizontal direction so that their axes of penetration intersect with the main surface of the semiconductor wafer W held on the susceptor 74. A transparent window 26 made of calcium fluoride material that transmits infrared light in a wavelength range measurable by the upper radiation thermometer 25 is attached to the end of the through hole 61a facing the heat treatment space 65. Furthermore, a transparent window 21 made of barium fluoride material that transmits infrared light in the wavelength range that can be measured by the lower radiation thermometer 20 is attached to the end of the through hole 61b facing the heat treatment space 65.

[0038] Gas supply holes 81 are formed in the upper part of the inner wall of the chamber 6 to supply processing gas to the heat treatment space 65. The gas supply holes 81 are formed at a position above the recess 62 and may be provided in the reflecting ring 68. The gas supply holes 81 are connected to a gas supply pipe 83 via a buffer space 82 formed in an annular shape inside the side wall of the chamber 6. The gas supply pipe 83 is connected to a processing gas supply source 85. A valve 84 is inserted in the gas supply pipe 83. When the valve 84 is opened, processing gas is supplied from the processing gas supply source 85 to the buffer space 82. The processing gas that has flowed into the buffer space 82 spreads within the buffer space 82, which has lower fluid resistance than the gas supply holes 81, and is supplied from the gas supply holes 81 into the heat treatment space 65. The processing gas may be, for example, an inert gas such as nitrogen (N), a reactive gas such as hydrogen (H) or ammonia (NH), or a mixture thereof (nitrogen gas in this embodiment).

[0039] Meanwhile, a gas exhaust hole 86 is formed in the lower part of the inner wall of the chamber 6 to exhaust gas from the heat treatment space 65. The gas exhaust hole 86 is formed below the recess 62 and may be provided in the reflecting ring 69. The gas exhaust hole 86 is connected to a gas exhaust pipe 88 via a buffer space 87 formed in an annular shape inside the side wall of the chamber 6. The gas exhaust pipe 88 is connected to an exhaust unit 190. A valve 89 is inserted in the gas exhaust pipe 88. When the valve 89 is opened, the gas in the heat treatment space 65 is exhausted from the gas exhaust hole 86 through the buffer space 87 to the gas exhaust pipe 88. The gas supply hole 81 and the gas exhaust hole 86 may be provided in multiple numbers along the circumferential direction of the chamber 6, or may be slit-shaped. The process gas supply source 85 and the exhaust unit 190 may be mechanisms provided in the heat treatment apparatus 1 or may be utilities of a factory where the heat treatment apparatus 1 is installed.

[0040] A gas exhaust pipe 191 for discharging gas from the heat treatment space 65 is also connected to the tip of the transfer opening 66. The gas exhaust pipe 191 is connected to an exhaust unit 190 via a valve 192. By opening the valve 192, the gas in the chamber 6 is exhausted through the transfer opening 66.

[0041] 3 is a perspective view showing the overall appearance of the holder 7. The holder 7 is configured to include a base ring 71, a connecting portion 72, and a susceptor 74. The base ring 71, the connecting portion 72, and the susceptor 74 are all made of quartz. In other words, the entire holder 7 is made of quartz.

[0042] The base ring 71 is an arc-shaped quartz member with a portion missing from the annular shape. This missing portion is provided to prevent interference between the base ring 71 and a transfer arm 11 of the transfer mechanism 10, which will be described later. The base ring 71 is placed on the bottom surface of the recess 62, and is supported by the wall surface of the chamber 6 (see FIG. 2). A plurality of connecting portions 72 (four in this embodiment) are erected on the upper surface of the base ring 71 along the circumferential direction of the annular shape. The connecting portions 72 are also quartz members, and are fixed to the base ring 71 by welding.

[0043] The susceptor 74 is supported by four connecting portions 72 provided on the base ring 71. FIG. 4 is a plan view of the susceptor 74. FIG. 5 is a cross-sectional view of the susceptor 74. The susceptor 74 includes a holding plate 75, a guide ring 76, and a plurality of substrate support pins 77. The holding plate 75 is a substantially circular, flat member made of quartz. The diameter of the holding plate 75 is larger than the diameter of the semiconductor wafer W. That is, the holding plate 75 has a planar size larger than that of the semiconductor wafer W.

[0044] A guide ring 76 is installed on the periphery of the upper surface of the holding plate 75. The guide ring 76 is an annular member having an inner diameter larger than the diameter of the semiconductor wafer W. For example, if the diameter of the semiconductor wafer W is φ300 mm, the inner diameter of the guide ring 76 is φ320 mm. The inner periphery of the guide ring 76 has a tapered surface that widens upward from the holding plate 75. The guide ring 76 is made of quartz, the same as the holding plate 75. The guide ring 76 may be welded to the upper surface of the holding plate 75, or may be fixed to the holding plate 75 by a separately processed pin or the like. Alternatively, the holding plate 75 and the guide ring 76 may be processed as an integrated member.

[0045] The area of ​​the upper surface of the holding plate 75 that is inside the guide ring 76 is a flat holding surface 75a that holds the semiconductor wafer W. A plurality of substrate support pins 77 are provided on the holding surface 75a of the holding plate 75. In this embodiment, a total of 12 substrate support pins 77 are provided at 30° intervals along a circumference concentric with the outer circumferential circle of the holding surface 75a (the inner circumferential circle of the guide ring 76). The diameter of the circle on which the 12 substrate support pins 77 are arranged (the distance between opposing substrate support pins 77) is smaller than the diameter of the semiconductor wafer W. If the diameter of the semiconductor wafer W is 300 mm, the diameter is 270 mm to 280 mm (270 mm in this embodiment). Each substrate support pin 77 is made of quartz. The plurality of substrate support pins 77 may be provided on the upper surface of the holding plate 75 by welding, or may be machined integrally with the holding plate 75.

[0046] Returning to FIG. 3 , four connecting portions 72 erected on the base ring 71 are fixed to the peripheral edge of the holding plate 75 of the susceptor 74 by welding. That is, the susceptor 74 and the base ring 71 are fixedly connected by the connecting portions 72. The base ring 71 of the holding portion 7 is supported on the wall surface of the chamber 6, and the holding portion 7 is thereby attached to the chamber 6. When the holding portion 7 is attached to the chamber 6, the holding plate 75 of the susceptor 74 is in a horizontal position (a position in which the normal line coincides with the vertical direction). That is, the holding surface 75a of the holding plate 75 is a horizontal plane.

[0047] The semiconductor wafer W carried into the chamber 6 is placed and held in a horizontal position on the susceptor 74 of the holder 7 attached to the chamber 6. At this time, the semiconductor wafer W is supported by twelve substrate support pins 77 erected on a holding plate 75 and held on the susceptor 74. More precisely, the upper ends of the twelve substrate support pins 77 contact the underside of the semiconductor wafer W to support the semiconductor wafer W. The heights of the twelve substrate support pins 77 (the distance from the upper ends of the substrate support pins 77 to the holding surface 75a of the holding plate 75) are uniform, so the twelve substrate support pins 77 can support the semiconductor wafer W in a horizontal position.

[0048] Furthermore, the semiconductor wafer W is supported by a plurality of substrate support pins 77 at a predetermined distance from the holding surface 75a of the holding plate 75. The thickness of the guide ring 76 is greater than the height of the substrate support pins 77. Therefore, the guide ring 76 prevents the semiconductor wafer W supported by the plurality of substrate support pins 77 from shifting in the horizontal direction.

[0049] 3 and 4, an opening 78 is formed in the holding plate 75 of the susceptor 74, penetrating vertically. The opening 78 is provided so that the lower radiation thermometer 20 can receive radiation (infrared light) emitted from the underside of the semiconductor wafer W. That is, the lower radiation thermometer 20 receives the light emitted from the underside of the semiconductor wafer W through the opening 78 and a transparent window 21 attached to the through-hole 61b of the chamber side 61, thereby measuring the temperature of the semiconductor wafer W. Furthermore, the holding plate 75 of the susceptor 74 is formed with four through-holes 79 through which lift pins 12 of the transfer mechanism 10, which will be described later, pass to transfer the semiconductor wafer W.

[0050] FIG. 6 is a plan view of the transfer mechanism 10. FIG. 7 is a side view of the transfer mechanism 10. The transfer mechanism 10 includes two transfer arms 11. The transfer arms 11 are arc-shaped so as to fit the generally annular recess 62. Two lift pins 12 are provided on each of the transfer arms 11. The transfer arms 11 and the lift pins 12 are made of quartz. Each transfer arm 11 is rotatable by a horizontal movement mechanism 13. The horizontal movement mechanism 13 horizontally moves the pair of transfer arms 11 between a transfer operation position (position indicated by a solid line in FIG. 6) where the transfer arms 11 transfer the semiconductor wafer W to the holder 7 and a retracted position (position indicated by a two-dot chain line in FIG. 6) where the transfer arms 11 do not overlap the semiconductor wafer W held by the holder 7 in a plan view. The horizontal movement mechanism 13 may be one that rotates each transfer arm 11 using an individual motor, or one that uses a link mechanism to rotate a pair of transfer arms 11 in conjunction with one another using a single motor.

[0051] Furthermore, the pair of transfer arms 11 are raised and lowered together with the horizontal movement mechanism 13 by the lifting mechanism 14. When the lifting mechanism 14 raises the pair of transfer arms 11 to the transfer operation position, a total of four lift pins 12 pass through through holes 79 (see FIGS. 3 and 4 ) formed in the susceptor 74, and the upper ends of the lift pins 12 protrude from the upper surface of the susceptor 74. On the other hand, when the lifting mechanism 14 lowers the pair of transfer arms 11 to the transfer operation position to remove the lift pins 12 from the through holes 79, and the horizontal movement mechanism 13 moves the pair of transfer arms 11 so as to open, each transfer arm 11 moves to a retracted position. The retracted position of the pair of transfer arms 11 is directly above the base ring 71 of the holder 7. Because the base ring 71 is placed on the bottom surface of the recess 62, the retracted position of the transfer arms 11 is inside the recess 62. In addition, an exhaust mechanism (not shown) is also provided near the location where the drive part of the transfer mechanism 10 (horizontal movement mechanism 13 and lifting mechanism 14) is located, and is configured to exhaust the atmosphere around the drive part of the transfer mechanism 10 to the outside of the chamber 6.

[0052] Returning to FIG. 2 , the flash heating unit 5, which is provided above the chamber 6, is configured with a light source made up of multiple (30 in this embodiment) xenon flash lamps FL inside a housing 51, and a reflector 52 provided to cover the light source from above. A lamp light emission window 53 is attached to the bottom of the housing 51 of the flash heating unit 5. The lamp light emission window 53, which forms the floor of the flash heating unit 5, is a plate-shaped quartz window made of quartz. By installing the flash heating unit 5 above the chamber 6, the lamp light emission window 53 faces the upper chamber window 63. The flash lamps FL irradiate a heat treatment space 65 with flash light from above the chamber 6 through the lamp light emission window 53 and the upper chamber window 63.

[0053] The multiple flash lamps FL are each a rod-shaped lamp having a long cylindrical shape, and are arranged in a plane so that their longitudinal directions are parallel to each other along the main surface of the semiconductor wafer W held by the holder 7 (i.e., along the horizontal direction). Therefore, the plane formed by the arrangement of the flash lamps FL is also a horizontal plane.

[0054] A xenon flash lamp FL comprises a rod-shaped glass tube (discharge tube) filled with xenon gas and fitted with an anode and cathode connected to a capacitor at both ends, and a trigger electrode attached to the outer surface of the glass tube. Because xenon gas is an electrical insulator, electricity does not flow through the glass tube under normal conditions, even if a charge is stored in the capacitor. However, when a high voltage is applied to the trigger electrode, causing the insulation to break down, the electricity stored in the capacitor flows instantaneously through the glass tube, exciting the xenon atoms or molecules and emitting light. In such a xenon flash lamp FL, electrostatic energy previously stored in the capacitor is converted into extremely short light pulses of 0.1 to 100 milliseconds, enabling it to emit extremely intense light compared to continuous light sources such as halogen lamps HL. In other words, a flash lamp FL is a pulsed lamp that emits light instantaneously for an extremely short period of time, less than one second. The light emission time of the flash lamp FL can be adjusted by adjusting the coil constant of the lamp power supply that supplies power to the flash lamp FL.

[0055] Furthermore, reflector 52 is provided above the multiple flash lamps FL so as to cover them entirely. The basic function of reflector 52 is to reflect the flash light emitted from the multiple flash lamps FL toward the heat treatment space 65. Reflector 52 is made of an aluminum alloy plate, and its surface (the surface facing the flash lamps FL) is roughened by blasting.

[0056] The halogen heating unit 4, which is provided below the chamber 6, has a plurality of halogen lamps HL (40 in this embodiment) built into the inside of the housing 41. The halogen heating unit 4 heats the semiconductor wafer W by irradiating light from the plurality of halogen lamps HL from below the chamber 6 through a lower chamber window 64 into a heat treatment space 65.

[0057] FIG. 8 is a plan view showing the arrangement of multiple halogen lamps HL. 40 halogen lamps HL are arranged in two rows, upper and lower. 20 halogen lamps HL are arranged in the upper row, which is closer to the holder 7, and 20 halogen lamps HL are also arranged in the lower row, which is farther from the holder 7 than the upper row. Each halogen lamp HL is a rod-shaped lamp having a long cylindrical shape. In both the upper and lower rows, the 20 halogen lamps HL are arranged so that their longitudinal directions are parallel to each other along the main surface of the semiconductor wafer W held by the holder 7 (i.e., along the horizontal direction). Therefore, the plane formed by the arrangement of the halogen lamps HL in both the upper and lower rows is a horizontal plane.

[0058] 8, the halogen lamps HL are arranged more densely in the region facing the periphery of the semiconductor wafer W held by the holder 7 on both the upper and lower tiers than in the region facing the center of the semiconductor wafer W. That is, on both the upper and lower tiers, the halogen lamps HL are arranged at a shorter pitch in the periphery of the lamp arrangement than in the center. This allows a greater amount of light to be irradiated onto the periphery of the semiconductor wafer W, which is prone to temperature drop during heating due to light irradiation from the halogen heating unit 4.

[0059] The lamp group consisting of the halogen lamps HL on the upper row and the lamp group consisting of the halogen lamps HL on the lower row are arranged so as to intersect in a grid pattern. That is, a total of 40 halogen lamps HL are arranged so that the longitudinal direction of the 20 halogen lamps HL arranged on the upper row and the longitudinal direction of the 20 halogen lamps HL arranged on the lower row are perpendicular to each other.

[0060] A halogen lamp HL is a filament-type light source that emits light by passing electricity through a filament placed inside a glass tube, causing it to incandescent. The glass tube is filled with an inert gas, such as nitrogen or argon, containing trace amounts of halogen elements (iodine, bromine, etc.). The introduction of halogen elements makes it possible to set the filament temperature at a high temperature while preventing filament breakage. Therefore, compared to standard incandescent light bulbs, halogen lamps HL have the characteristics of a longer lifespan and the ability to continuously emit strong light. In other words, halogen lamps HL are continuous lamps that emit light continuously for at least one second. Furthermore, because halogen lamps HL are rod-shaped, they have a long lifespan, and by arranging them horizontally, they achieve excellent radiation efficiency toward the semiconductor wafer W above.

[0061] Also, a reflector 43 is provided below the two-tiered halogen lamps HL within the housing 41 of the halogen heating unit 4 (FIG. 2). The reflector 43 reflects the light emitted from the multiple halogen lamps HL toward the heat treatment space 65.

[0062] As shown in FIG. 2, the chamber 6 is provided with two radiation thermometers (pyrometers in this embodiment): an upper radiation thermometer 25 and a lower radiation thermometer 20. The upper radiation thermometer 25 is installed diagonally above the semiconductor wafer W held on the susceptor 74 and receives infrared light radiated from the top surface of the semiconductor wafer W to measure the temperature of the top surface. The infrared sensor 29 of the upper radiation thermometer 25 is equipped with an InSb (indium antimonide) optical element so as to be able to respond to a sudden temperature change on the top surface of the semiconductor wafer W at the moment when the flash light is irradiated. On the other hand, the lower radiation thermometer 20 is installed diagonally below the semiconductor wafer W held on the susceptor 74 and receives infrared light radiated from the bottom surface of the semiconductor wafer W to measure the temperature of the bottom surface.

[0063] The control unit 3 controls the various operating mechanisms provided in the heat treatment apparatus 1. FIG. 9 is a block diagram showing the configuration of the control unit 3. The hardware configuration of the control unit 3 is similar to that of a general computer. That is, the control unit 3 includes a CPU, which is a circuit that performs various arithmetic processing, a ROM, which is a read-only memory that stores basic programs, a RAM, which is a readable and writable memory that stores various information, and a storage unit 34 (e.g., a magnetic disk) that stores control software, data, and the like. The CPU of the control unit 3 executes a predetermined processing program, causing the processing in the heat treatment apparatus 1 to proceed.

[0064] The control unit 3 includes a temperature distribution prediction unit 31. The temperature distribution prediction unit 31 is a functional processing unit that is realized by the CPU of the control unit 3 executing a predetermined processing program. The processing content of the temperature distribution prediction unit 31 will be described in further detail below. In addition, the memory unit 34 of the control unit 3 stores a composite function 120 (FIG. 13) that combines the first trained regression model and the second trained regression model.

[0065] Elements such as the halogen lamp HL are electrically connected to the control unit 3. The control unit 3 controls the output of the halogen lamp HL (strictly speaking, it controls the power source that supplies power to the halogen lamp HL).

[0066] A display unit 37 and an input unit 36 ​​are also connected to the control unit 3. The display unit 37 and the input unit 36 ​​function as a user interface for the heat treatment device 1. The control unit 3 displays various information on the display unit 37. An operator of the heat treatment device 1 can input various commands and parameters from the input unit 36 ​​while checking the information displayed on the display unit 37. The input unit 36 ​​can be, for example, a keyboard or a mouse. The display unit 37 can be, for example, a liquid crystal display. In this embodiment, a liquid crystal touch panel provided on the outer wall of the heat treatment device 1 is used as the display unit 37 and the input unit 36, thereby combining the functions of both.

[0067] In addition to the above configuration, the heat treatment apparatus 1 is equipped with various cooling structures to prevent excessive temperature rise in the halogen heating unit 4, flash heating unit 5, and chamber 6 due to the thermal energy generated by the halogen lamps HL and flash lamps FL during heat treatment of the semiconductor wafer W. For example, a water-cooled pipe (not shown) is provided in the wall of the chamber 6. The halogen heating unit 4 and flash heating unit 5 also have an air-cooled structure that creates a gas flow inside to remove heat. Air is also supplied to the gap between the upper chamber window 63 and the lamp light emission window 53 to cool the flash heating unit 5 and upper chamber window 63.

[0068] Next, we will explain the processing content in the heat treatment system 100. First, we will explain the processing for a normal semiconductor wafer W in the heat treatment apparatus 1. The processing procedure for the semiconductor wafer W described below progresses as the control unit 3 controls each operating mechanism of the heat treatment apparatus 1.

[0069] First, prior to processing of the semiconductor wafer W, the gas supply valve 84 is opened, and the exhaust valve 89 is also opened to start supplying and exhausting gas to and from the chamber 6. When the valve 84 is opened, nitrogen gas is supplied to the heat treatment space 65 through the gas supply hole 81. When the valve 89 is opened, the gas inside the chamber 6 is exhausted through the gas exhaust hole 86. As a result, the nitrogen gas supplied from the upper part of the heat treatment space 65 inside the chamber 6 flows downward and is exhausted from the lower part of the heat treatment space 65.

[0070] Furthermore, by opening the valve 192, the gas inside the chamber 6 is also exhausted from the transfer opening 66. Furthermore, the atmosphere around the drive unit of the transfer mechanism 10 is also exhausted by an exhaust mechanism (not shown). Note that during the heat treatment of the semiconductor wafer W in the heat treatment apparatus 1, nitrogen gas is continuously supplied to the heat treatment space 65, and the supply amount is changed as appropriate depending on the treatment process.

[0071] Next, gate valve 185 is opened to open transfer opening 66, and a semiconductor wafer W to be processed is carried into heat treatment space 65 in chamber 6 through transfer opening 66 by a transfer robot outside the apparatus. At this time, there is a risk that the atmosphere outside the apparatus may be drawn in as the semiconductor wafer W is carried in, but since nitrogen gas is continuously supplied to chamber 6, the nitrogen gas flows out from transfer opening 66, making it possible to minimize the drawing in of such external atmosphere.

[0072] The semiconductor wafer W carried in by the transfer robot advances to a position directly above the holder 7 and stops there. Then, the pair of transfer arms 11 of the transfer mechanism 10 move horizontally from the retracted position to the transfer operation position and rise, causing the lift pins 12 to pass through the through holes 79 and protrude from the upper surface of the holding plate 75 of the susceptor 74 to receive the semiconductor wafer W. At this time, the lift pins 12 rise to a position higher than the upper ends of the substrate support pins 77.

[0073] After the semiconductor wafer W is placed on the lift pins 12, the transfer robot exits the heat treatment space 65, and the transfer opening 66 is closed by the gate valve 185. Then, the pair of transfer arms 11 descend, transferring the semiconductor wafer W from the transfer mechanism 10 to the susceptor 74 of the holder 7, where it is held horizontally from below. The semiconductor wafer W is supported by a plurality of substrate support pins 77 erected on the holding plate 75 and held on the susceptor 74. The semiconductor wafer W is held on the holder 7 with its front surface, which is the surface to be processed, facing upward. A predetermined gap is formed between the back surface (the main surface opposite to the front surface) of the semiconductor wafer W supported by the plurality of substrate support pins 77 and the holding surface 75a of the holding plate 75. The pair of transfer arms 11, which have descended to below the susceptor 74, are retracted to a retracted position, i.e., inside the recess 62, by the horizontal movement mechanism 13.

[0074] After the semiconductor wafer W is held from below in a horizontal position by the susceptor 74 of the holder 7, which is made of quartz, the 40 halogen lamps HL of the halogen heating unit 4 are turned on all at once to begin preheating (assisted heating). The halogen light emitted from the halogen lamps HL passes through the lower chamber window 64 and the susceptor 74, both of which are made of quartz, and is irradiated onto the underside of the semiconductor wafer W. The semiconductor wafer W is preheated by being irradiated with light from the halogen lamps HL, and its temperature rises. Note that the transfer arm 11 of the transfer mechanism 10 is retracted inside the recess 62, so it does not interfere with heating by the halogen lamps HL.

[0075] The temperature of the semiconductor wafer W, which is heated by the light irradiation from the halogen lamps HL, is measured by the lower radiation thermometer 20. The measured temperature of the semiconductor wafer W is transmitted to the control unit 3. The control unit 3 controls the output of the halogen lamps HL while monitoring whether the temperature of the semiconductor wafer W, which is heated by the light irradiation from the halogen lamps HL, has reached a predetermined preheating temperature T1. In other words, the control unit 3 feedback-controls the output of the halogen lamps HL based on the value measured by the lower radiation thermometer 20 so that the temperature of the semiconductor wafer W becomes the preheating temperature T1.

[0076] After the temperature of the semiconductor wafer W reaches the preheating temperature T1, the control unit 3 temporarily maintains the semiconductor wafer W at the preheating temperature T1. Specifically, when the temperature of the semiconductor wafer W measured by the lower radiation thermometer 20 reaches the preheating temperature T1, the control unit 3 adjusts the output of the halogen lamps HL to maintain the temperature of the semiconductor wafer W at approximately the preheating temperature T1.

[0077] By performing preheating using the halogen lamps HL in this manner, the temperature of the entire semiconductor wafer W is uniformly raised to the preheating temperature T1. During preheating using the halogen lamps HL, the temperature of the peripheral portion of the semiconductor wafer W, where heat dissipation is more likely, tends to be lower than that of the central portion, but the arrangement density of the halogen lamps HL in the halogen heating unit 4 is higher in the region facing the peripheral portion of the semiconductor wafer W than in the region facing the central portion. As a result, a greater amount of light is irradiated onto the peripheral portion of the semiconductor wafer W, where heat dissipation is more likely, and the in-plane temperature distribution of the semiconductor wafer W during the preheating stage can be made uniform.

[0078] When a predetermined time has elapsed since the temperature of the semiconductor wafer W reached the preheating temperature T1, the flash lamps FL of the flash heating unit 5 irradiate the surface of the semiconductor wafer W held on the susceptor 74 with flash light. At this time, part of the flash light emitted from the flash lamps FL heads directly into the chamber 6, and the other part is reflected by the reflector 52 before heading into the chamber 6, and the semiconductor wafer W is flash-heated by the irradiation of these flash lights.

[0079] Flash heating is performed by irradiating a flash of light (flash of light) from flash lamps FL, which can raise the surface temperature of the semiconductor wafer W in a short time. That is, the flash of light irradiated from the flash lamps FL is an extremely short, intense flash with an irradiation time of approximately 0.1 milliseconds to 100 milliseconds, in which electrostatic energy previously stored in a capacitor is converted into an extremely short light pulse. The surface temperature of the semiconductor wafer W flash-heated by the irradiation of the flash light from the flash lamps FL instantaneously rises to a processing temperature T2 of 1000°C or higher and then rapidly drops.

[0080] After the flash heating process is completed, the halogen lamps HL are turned off after a predetermined time has elapsed. This causes the temperature of the semiconductor wafer W to rapidly decrease from the preheating temperature T1. The temperature of the semiconductor wafer W during this decrease is measured by the lower radiation thermometer 20, and the measurement result is transmitted to the control unit 3. The control unit 3 monitors, based on the measurement result of the lower radiation thermometer 20, whether the temperature of the semiconductor wafer W has decreased to a predetermined temperature. After the temperature of the semiconductor wafer W has decreased to or below the predetermined temperature, the pair of transfer arms 11 of the transfer mechanism 10 again move horizontally from the retracted position to the transfer operation position and rise, causing the lift pins 12 to protrude from the upper surface of the susceptor 74 and receive the heat-treated semiconductor wafer W from the susceptor 74. Next, the transfer opening 66, which had been closed by the gate valve 185, is opened, and the semiconductor wafer W placed on the lift pins 12 is removed from the chamber 6 by a transfer robot external to the apparatus, completing the heat treatment of the semiconductor wafer W.

[0081] In the heat treatment system 100, a model is generated that predicts the temperature distribution that occurs on the semiconductor wafer W during light irradiation processing in the heat treatment device 1. Model generation in the heat treatment system 100 is roughly divided into three stages: construction of a first trained regression model using optical simulation, construction of a second trained regression model based on actual measurement data, and use of a composite function 120 that combines the two.

[0082] Fig. 10 is a flowchart showing the procedure for constructing a first trained regression model. Fig. 13 is a diagram for conceptually explaining the overall processing content in heat treatment system 100. In Fig. 13, the processing in the area surrounded by the dotted line indicated by symbol A1 is the processing for constructing the first trained regression model.

[0083] First, various parameters for the simulation are input to the optical simulator 101 (step S11). The optical simulator 101 executes a simulation to determine the irradiance distribution on the light-receiving surface (back surface) of the semiconductor wafer W when the semiconductor wafer W, housed in the chamber 6 of the heat treatment apparatus 1 and held on the susceptor 74, is irradiated with light from the 40 halogen lamps HL. In step S11, various conditions related to light irradiation from the halogen lamps HL required to execute the optical simulation are input as parameters. Specifically, the arrangement of the 40 halogen lamps HL, the wavelength of the light irradiated from the halogen lamps HL, the shape and optical constants of the chamber 6, the optical constants of the light-receiving surface of the semiconductor wafer W, and the input power to each halogen lamp HL are input to the optical simulator 101 as parameters.

[0084] The optical simulator 101 performs an optical simulation using the input parameters to calculate the irradiance distribution on the light-receiving surface of the semiconductor wafer W (step S12). At this time, the optical simulator 101 calculates the irradiance distribution on the entire surface of the semiconductor wafer W, including both the central portion and the peripheral portion.

[0085] The processes of steps S11 and S12 are repeated multiple times (e.g., 200 times or more) by changing the light irradiation conditions (e.g., changing the power input to each halogen lamp HL). The combination of parameters to be input is determined by determining the upper and lower limits of each parameter (e.g., the minimum and maximum values ​​of the power input to the halogen lamp HL) and then using a combination of processing conditions determined by a space-filling experimental design. The large set of parameters and irradiance distribution obtained in this way becomes the training data for machine learning.

[0086] Next, the first regression learner 102 performs machine learning using the light irradiation conditions input to the optical simulator 101 in step S11 as input variables and the irradiance distribution calculated by the optical simulator 101 in step S12 as output variables (step S13). That is, the first regression learner 102 performs regression model learning using the measurement point coordinates, the power input to each halogen lamp HL, the optical constants inside the chamber 6, and the optical constants of the semiconductor wafer W as input variables and the irradiance distribution obtained by the optical simulation as an output variable. The first regression learner 102 performs machine learning using an algorithm such as a neural network with a recurrent layer, a decision tree, an SVM (Support Vector Machine), or ensemble learning.

[0087] The first regression learner 102 constructs a first trained regression model 150 by the above-described machine learning (step S14). The first trained regression model 150, generated by machine learning based on learning data obtained by optical simulation, outputs an irradiance distribution occurring within the surface of the semiconductor wafer W in response to appropriate input values ​​related to light irradiation from the halogen lamp HL. The irradiance distribution output by the first trained regression model 150 also includes the irradiance at the peripheral edge of the semiconductor wafer W.

[0088] Next, Fig. 11 is a flowchart showing the procedure for constructing a second trained regression model. In Fig. 13, the processing in the area surrounded by the dotted line indicated by symbol A2 is the processing for constructing the second trained regression model.

[0089] When constructing the second trained regression model, a monitor wafer is actually subjected to light irradiation heat treatment in the heat treatment device 1 (step S21). The monitor wafer is a silicon wafer having a disk shape similar to that of the semiconductor wafer W to be produced, and has the same size and shape as the semiconductor wafer W. The monitor wafer has not been subjected to pattern formation or film deposition treatment, but has been doped with impurities.

[0090] In the heat treatment apparatus 1, the monitor wafer held on the susceptor 74 is heated by irradiating it with light from 40 halogen lamps HL. The processing conditions for the light irradiation processing of the monitor wafer in the heat treatment apparatus 1 are preferably selected from the multiple conditions used when the optical simulator 101 described above calculates the irradiance distribution.

[0091] The monitor wafer subjected to light irradiation heating in the heat treatment apparatus 1 is loaded into the substrate measuring instrument 103. The substrate measuring instrument 103 measures the sheet resistance of the monitor wafer after the light irradiation heating process. The substrate measuring instrument 103 measures the sheet resistance at multiple locations on the surface of the monitor wafer to determine the in-plane distribution of resistance. The sheet resistance is a function of the temperature reached by the monitor wafer during the light irradiation heating process. That is, the sheet resistance can be converted into the temperature reached by the monitor wafer using a predetermined conversion formula. This allows the in-plane temperature distribution of the monitor wafer during the light irradiation processing in the heat treatment apparatus 1 to be obtained from the measurement results of the substrate measuring instrument 103 (step S22). Note that the temperature of the monitor wafer undergoing light irradiation heating is also measured by the upper radiation thermometer 25 and the lower radiation thermometer 20. However, because these radiation thermometers only measure the temperature of a limited measurement area of ​​the monitor wafer, the in-plane temperature distribution cannot be obtained from both radiation thermometers.

[0092] Steps S21 and S22 are repeatedly performed on multiple (e.g., approximately 10) monitor wafers under different light irradiation conditions. The data set of light irradiation processing conditions and measured temperature distributions obtained in this manner serves as learning data for machine learning. Note that preprocessing for machine learning may be performed on the learning data obtained by actual measurement. For example, a value determined to be an outlier may be taken as the average value of values ​​at adjacent coordinates. Furthermore, if there is an error due to individual differences in the substrate measuring instrument 103 or the monitor wafer, data normalization may be performed.

[0093] Next, the second regression learner 104 performs machine learning using the light irradiation processing conditions performed in the heat treatment apparatus 1 as input variables and the temperature distribution obtained by actual measurement as output variables (step S23). Specifically, the second regression learner 104 uses the measurement point coordinates, irradiance distribution, physical constants of the monitor wafer, the elapsed time since the start of processing, and the time-varying input power to each halogen lamp HL as input variables, and performs regression model learning using the temperature distribution obtained by actual measurement as output variables. The input variables for the machine learning in step S23 include the irradiance distribution. The irradiance distribution calculated by optical simulation in step S12 can be used as the irradiance distribution. The second regression learner 104 also performs machine learning using algorithms such as a neural network with a recurrent layer, a decision tree, SVM, and ensemble learning. The input variables may also include the wafer temperature measured by the upper or lower radiation thermometer 25 or 20 during the light irradiation heat treatment of the monitor wafer as an auxiliary parameter.

[0094] The second regression learner 104 constructs a second trained regression model 160 through the above-described machine learning (step S24). While the first trained regression model 150 is based on optical simulation, the second trained regression model 160 is a regression model based on actual measurement data. The second trained regression model 160, generated by machine learning based on learning data obtained through actual measurement, outputs a temperature distribution occurring within the surface of the semiconductor wafer W in response to appropriate input values ​​including an irradiance distribution. The temperature distribution output by the second trained regression model 160 also includes the temperature of the peripheral edge of the semiconductor wafer W.

[0095] 12 is a flowchart showing the procedure for combining and utilizing the first trained regression model 150 and the second trained regression model 160. The first trained regression model 150 based on optical simulation and the second trained regression model 160 based on actual measurement data are combined to derive the composite function 120 (step S31). The derivation of the composite function 120 may be performed, for example, by the control unit 3 of the heat treatment device 1 that receives the first trained regression model 150 and the second trained regression model 160 from the first regression learner 102 and the second regression learner 104, respectively, or by another computer.

[0096] The first trained regression model 150 outputs the irradiance distribution on the semiconductor wafer W using conditions related to light irradiation from the halogen lamps HL as input variables. Meanwhile, the second trained regression model 160 outputs the temperature distribution occurring on the semiconductor wafer W using processing conditions in the heat treatment apparatus 1, including the irradiance distribution, as input variables. The irradiance distribution output from the first trained regression model 150 is transferred as part of the input variables of the second trained regression model 160, and the first trained regression model 150 and the second trained regression model 160 are combined to derive a composite function 120. The composite function 120 is a regression model that uses measurement point coordinates, input power to each halogen lamp HL, optical constants within the chamber 6, optical constants of the semiconductor wafer W, and optical constants of the monitor wafer as input variables, and the temperature distribution occurring on the semiconductor wafer W as an output variable. The generated composite function 120 is stored, for example, in the memory unit 34 of the control unit 3 (FIG. 9).

[0097] Before processing the semiconductor wafer W in the heat treatment apparatus 1, the temperature distribution prediction unit 31 of the control unit 3 predicts the temperature distribution that will occur in the semiconductor wafer W during the light irradiation heat treatment using the composite function 120 (step S32). Specifically, the temperature distribution prediction unit 31 obtains time-series data of the temperature distribution that will occur in the semiconductor wafer W by inputting processing conditions such as the power input to each halogen lamp HL into the composite function 120. The control unit 3 may display the temperature distribution of the semiconductor wafer W predicted from the composite function 120 on the display unit 37.

[0098] In this embodiment, the multi-objective optimizer 105 optimizes the processing conditions based on the temperature distribution of the semiconductor wafer W predicted from the composite function 120 (step S33). The multi-objective optimizer 105 derives, as an evaluation function, the difference between the target temperature distribution for the semiconductor wafer W to be processed and the temperature distribution of the semiconductor wafer W predicted from the composite function 120. The multi-objective optimizer 105 performs multi-objective optimization so as to minimize the evaluation function at each time point when the temperature distribution is predicted, and determines the processing conditions for the semiconductor wafer W to be processed by inversely calculating the input variables of the composite function 120 (step S34).

[0099] By performing a heat treatment on the semiconductor wafer W in the heat treatment apparatus 1 according to the treatment conditions determined in this manner, it becomes possible to set the temperature distribution occurring on the semiconductor wafer W to a target value. Note that the multi-objective optimizer 105 may be the control unit 3 of the heat treatment apparatus 1 or another computer.

[0100] In this embodiment, a first trained regression model 150 created based on optical simulation and a second trained regression model 160 created based on actual measurements are combined to derive a composite function 120. Then, the temperature distribution occurring in a semiconductor wafer W during light irradiation heat treatment is predicted from the composite function 120.

[0101] The temperature distribution occurring in a semiconductor wafer W during light irradiation heat treatment is determined by the irradiance distribution and the energy balance due to heat transfer factors such as thermal conduction, convection, and radiation. Of these, the irradiance distribution is the dominant factor that determines the temperature distribution. In this embodiment, the irradiance distribution, which is the dominant factor that determines the temperature distribution, is derived from a first trained regression model 150 created based on optical simulation, and the heat transfer factors are left to a second trained regression model 160 created based on actual measurements.

[0102] Developing a regression equation for determining the temperature distribution occurring on a semiconductor wafer W solely through actual measurements would consume a huge amount of monitor wafers. In this embodiment, the irradiance distribution, which is the dominant factor determining the temperature distribution, is derived from the first trained regression model 150 created based on optical simulation, and only the heat transfer element is left to the second trained regression model 160 created based on actual measurements, thereby significantly reducing the amount of monitor wafers consumed.

[0103] Furthermore, because the irradiance distribution is derived from the first trained regression model 150 created based on optical simulation, an accurate irradiance distribution can also be obtained for the peripheral portion of the semiconductor wafer W. As a result, it is possible to improve the accuracy of predicting the temperature distribution at the peripheral portion of the semiconductor wafer W. In other words, by following this embodiment, it is possible to predict the temperature distribution occurring in the semiconductor wafer W easily and with high accuracy.

[0104] Although the embodiments of the present invention have been described above, various modifications can be made to the present invention without departing from the spirit of the present invention. For example, during actual light irradiation heat treatment of a semiconductor wafer W, the control unit 3 may sequentially provide input variables to the composite function 120 in real time to output a predicted temperature value at any coordinate on the semiconductor wafer W, and feedback control the temperature of the semiconductor wafer W based on the predicted temperature value. Specifically, the control unit 3 controls the output of the halogen lamp HL so that the predicted temperature value obtained from the composite function 120 reaches a predetermined target value. In other words, control is performed using the composite function 120 as a virtual temperature sensor. This enables temperature control at coordinate positions on the semiconductor wafer W that cannot be measured by the upper pyrometer 25 or the lower pyrometer 20.

[0105] Alternatively, the temperature of the semiconductor wafer W during the light irradiation heat treatment may be measured by the upper radiation thermometer 25 or the lower radiation thermometer 20, and the control unit 3 may obtain the predicted temperature values ​​of the coordinates of the measurement points from the composite function 120. The control unit 3 may then compare the actual measurement value from the upper radiation thermometer 25 or the lower radiation thermometer 20 with the predicted temperature value output from the composite function 120, and re-learn the composite function 120 so that the two match. In other words, the actual measurement value from the upper radiation thermometer 25 or the lower radiation thermometer 20 is used as training data to re-learn the composite function 120.

[0106] Furthermore, in the above embodiment, the flash heating unit 5 is provided with 30 flash lamps FL, but this is not limited to this and the number of flash lamps FL can be any number. Furthermore, the flash lamps FL are not limited to xenon flash lamps and may be krypton flash lamps. Furthermore, the number of halogen lamps HL provided in the halogen heating unit 4 is not limited to 40 and can be any number.

[0107] Furthermore, in the above embodiment, the preheating process of the semiconductor wafer W is performed using a filament-type halogen lamp HL as a continuously lit lamp that emits light continuously for one second or more, but this is not limited to this, and the preheating process may also be performed using a discharge-type arc lamp (e.g., a xenon arc lamp) or an LED lamp as a continuously lit lamp instead of the halogen lamp HL. [Explanation of symbols]

[0108] 1. Heat treatment equipment 3. Control Unit 4 Halogen heating section 5 Flash heating section 6 chambers 7 Holding part 10 Transfer mechanism 20 Lower radiation thermometer 25 Upper radiation thermometer 31 Temperature distribution prediction unit 63 Upper chamber window 64 Lower chamber window 65 Heat Treatment Space 74 Susceptor 100 Heat Treatment System 101 Optical Simulator 102 First Regression Learner 103 Substrate measuring instrument 104 Second Regression Learner 105 Multi-objective optimizer 120 Composite Functions 150 First trained regression model 160 Second trained regression model FL flash lamp HL halogen lamp W Semiconductor wafer

Claims

1. A heat treatment method for heating a substrate by irradiating the substrate with light, comprising: an irradiance distribution calculation step of calculating an irradiance distribution on the substrate by optical simulation based on conditions related to light irradiation from the lamp; a first learning step of constructing a first trained model by machine learning using the conditions related to the light irradiation as input variables and the irradiance distribution calculated by the optical simulation as an output variable; a temperature distribution measuring step of measuring a temperature distribution occurring on the monitor substrate when the monitor substrate is irradiated with light from the lamp; a second learning process of constructing a second trained model by machine learning using processing conditions in the temperature distribution measuring process including an irradiance distribution as input variables and the temperature distribution measured in the temperature distribution measuring process as an output variable; a combining step of combining the first trained model and the second trained model by transferring the irradiance distribution output from the first trained model as a part of the input variables of the second trained model, thereby deriving a composite function; a temperature distribution prediction step of predicting a temperature distribution that occurs on the substrate to be processed when the substrate to be processed is irradiated with light from the lamps based on the composite function; A heat treatment method comprising:

2. The heat treatment method according to claim 1, a heat treatment method, characterized in that the lamp output is controlled based on the predicted temperature value predicted in the temperature distribution prediction step;

3. The heat treatment method according to claim 1, a heat treatment method characterized in that the difference between a target temperature distribution for the substrate to be treated and the temperature distribution predicted in the temperature distribution prediction step is used as an evaluation function, and processing conditions for the substrate to be treated are determined so that the evaluation function is minimized.

4. A thermal processing system that heats a substrate by irradiating the substrate with light, comprising: an optical simulator that calculates an irradiance distribution on the substrate by optical simulation based on conditions related to light irradiation from the lamp; a first learner that constructs a first trained model by machine learning using the conditions related to the light irradiation as input variables and the irradiance distribution calculated by the optical simulation as an output variable; and a temperature distribution measuring device for measuring a temperature distribution occurring on the monitor substrate when the monitor substrate is irradiated with light from the lamp in the heat treatment device; a second learning device that constructs a second trained model by machine learning using processing conditions during light irradiation of the monitor substrate, including an irradiance distribution, as input variables and a temperature distribution measured by the temperature distribution measuring device as an output variable; and Equipped with A thermal processing system characterized by predicting the temperature distribution that occurs on a substrate to be processed when light is irradiated from the lamp in the thermal processing apparatus based on a composite function derived by combining the first trained model and the second trained model by passing the irradiance distribution output from the first trained model as part of the input variables of the second trained model.

5. 5. The heat treatment system of claim 4, A thermal processing system, characterized in that the output of the lamps is controlled based on a predicted value of a temperature that is predicted to occur in the substrate to be processed.

6. 5. The heat treatment system of claim 4, a heat treatment system characterized in that an evaluation function is defined as a difference between a target temperature distribution for the substrate to be treated and a predicted temperature distribution that will occur in the substrate to be treated, and processing conditions for the substrate to be treated are determined so that the evaluation function is minimized.

7. A heat treatment apparatus that heats a substrate by irradiating the substrate with light, a chamber for housing the substrate; a holder that holds the substrate in the chamber; a lamp that irradiates light onto the substrate held by the holder; a control unit for controlling the output of the lamp; Equipped with A heat treatment apparatus characterized in that the control unit predicts the temperature distribution that occurs on the substrate to be treated when light is irradiated from the lamp to the substrate to be treated, based on a composite function derived by combining a first trained model constructed by machine learning with conditions related to light irradiation from the lamp as input variables and an irradiance distribution calculated by optical simulation based on the conditions as output variables, and a second trained model constructed by machine learning with processing conditions when light is irradiated from the lamp to the monitor substrate in the chamber as input variables and a temperature distribution that occurs on the monitor substrate as output variables.

8. 8. The heat treatment apparatus according to claim 7, A heat treatment apparatus characterized in that the composite function is derived by combining the first trained model and the second trained model by passing the irradiance distribution output from the first trained model as part of the input variables of the second trained model.

9. 8. The heat treatment apparatus according to claim 7, The heat treatment apparatus is characterized in that the control unit controls the output of the lamps based on a predicted value of a temperature that is predicted to occur in the substrate to be treated.

10. 8. The heat treatment apparatus according to claim 7, The control unit uses an evaluation function that is the difference between a target temperature distribution for the substrate to be processed and a predicted temperature distribution that will occur in the substrate to be processed, and determines processing conditions for the substrate to be processed so that the evaluation function is minimized.

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