DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 18 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nishimura et al. [US 2019/0361361 A1].
Regarding claims 1, 18 and 19, Nishimura et al. discloses an extreme ultraviolet light generation system / an electronic device manufacturing method (Figs. 1 and 12) configured to generate a mist-like target by irradiating (Fig. 3, 277), with prepulse laser light, a droplet target generated by combining a plurality of droplets, cause plasma to be generated by irradiating the mist-like target with main pulse laser light (paragraph [0052]teaches the burst region), and generate extreme ultraviolet light, the extreme ultraviolet light generation system comprising:
a pulse laser light sensor (501) configured to measure a pulse energy of the main pulse laser light (paragraph [0145] teaches the laser light sensor);
a target detection sensor (41) configured to generate a passage signal of the droplet target for generating a trigger signal for irradiation with the main pulse laser light (paragraphs [0069]-[0071] teaches the target detecting sensor);
an EUV light sensor (43) configured to measure a pulse energy of the extreme ultraviolet light (paragraph [072] teaches the EUV light sensor); and
a processor (8), the processor including a neural network (paragraph [0074]) which receives, as input data, log data of the pulse energy obtained from the pulse laser light sensor (paragraph [0145]), log data of an irradiation pulse interval of the main pulse laser light (paragraphs [0078]-[0080]), and log data of the pulse energy obtained from the EUV light sensor (paragraph [0111]) and generates, as output data, information enabling to identify which state the extreme ultraviolet light generation system is in among a normal state, a state in which combining failure of the droplet targets is occurring, a state in which a variation of intervals between the droplet targets is abnormal, and a state in which a relative position between an irradiation position with the main pulse laser light and the mist-like target is abnormal (paragraphs [0114]-[0145] teaches identifying which state the extreme ultraviolet light generation system is in among a normal state and state of various errors, see also Figs. 4-11).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-17 are rejected under 35 U.S.C. 103 as being unpatentable over Nishimura et al. in view of Chen et al. [US 2023/0049820 A1].
Regarding claims 2-17, Nishimura et al. discloses the system / method comprising a computer in which hardware such as a processor and software such as a program module are combined, storing data and providing feedback (paragraphs [0074], [0092], [0111]).
Nishimura et al. does not teach, further comprising a terminal configured to display the information, wherein the terminal displays output values indicating probabilities of being in the states for each state, wherein the output values are relative values in a numerical range in which 1 is a maximum value and 0 is a minimum value, and a sum of the output values of the respective states is 1, wherein the processor executes a process of estimating which state the extreme ultraviolet light generation system is in using the neural network at s specific time interval, and the terminal displays, for each estimation time, a state with the output value being maximum, wherein the respective log data are measured in a same time period, wherein the time period is 3 to 8 seconds, wherein a number of pieces of data in the time period of each log data is 500 to 1500, wherein, when the number of pieces of data is different for each log data, the number of pieces of data of each log data is adjusted to be the same, wherein the number of pieces of data in the time period of each log data is the same, wherein an intermediate layer of the neural network includes a convolutional network, wherein an activation function of an intermediate layer of the neural network is a ReLU function, wherein an intermediate layer of the neural network includes a process of max pooling, wherein an output layer of the neural network includes a fully connected layer, wherein an activation function of the output layer of the neural network is a sigmoid function, wherein the neural network is a neural network learned by using teacher data in which the states and the log data are associated respectively, wherein the teacher data includes first teacher data which is the log data when the extreme ultraviolet light generation system is in a normal state and second teacher data which is the log data when the extreme ultraviolet light generation system is in an abnormal state.
However, Chen et al. discloses a dual-feedback control system for laser beam targeting in a lithography system such as an EUV lithography system comprising a controller (paragraph [0081]) for executing machine learning model may including one or more of a neural network model, a random forest model, a clustering model, or a regression model, among other examples and using the machine learning model to determine a likelihood, probability, or confidence that a particular outcome (e.g., an amount of an increase in EUV radiation or an increase target position accuracy, among other examples) for a subsequent exposure operation will be achieved using the candidate parameters (paragraphs [0069]-[0072]).
Therefore, it would have been obvious to one of ordinary skills in the art to provide a neutral network for executing machine learning models including one or more models/functions, as taught by Chen et al. in the system of Nishimura et al. because such a modification provides a EUV radiation source that maintains an accurate target position of the pre-pulse laser beam to maintain a designated EUV radiation dose, a targeted yield rate or quality of semiconductor devices manufactured using the EUV radiation source, and/or an efficient use of the target material, among other examples (paragraph [0124] of Chen et al.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEORAM PERSAUD whose telephone number is (571)270-5476. The examiner can normally be reached M-F 8AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Minh-Toan Ton can be reached at 571-272-2303. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DEORAM PERSAUD/ Primary Examiner, Art Unit 2882
Prosecution Insights