CN106919982A - A kind of method for diagnosing faults towards semiconductor manufacturing equipment - Google Patents
A kind of method for diagnosing faults towards semiconductor manufacturing equipment Download PDFInfo
- Publication number
- CN106919982A CN106919982A CN201710166248.9A CN201710166248A CN106919982A CN 106919982 A CN106919982 A CN 106919982A CN 201710166248 A CN201710166248 A CN 201710166248A CN 106919982 A CN106919982 A CN 106919982A
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- rule
- rete
- semiconductor manufacturing
- manufacturing equipment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 239000004065 semiconductor Substances 0.000 title claims abstract description 34
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 32
- 238000003745 diagnosis Methods 0.000 claims abstract description 40
- 238000012544 monitoring process Methods 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000012360 testing method Methods 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 8
- 238000012937 correction Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000019771 cognition Effects 0.000 description 2
- 238000009795 derivation Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/048—Fuzzy inferencing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/046—Forward inferencing; Production systems
- G06N5/047—Pattern matching networks; Rete networks
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Fuzzy Systems (AREA)
- Automation & Control Theory (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Test And Diagnosis Of Digital Computers (AREA)
Abstract
The invention discloses a kind of method for diagnosing faults towards semiconductor manufacturing equipment, the expert system including fuzzy rule base, inference machine is initially set up；Further, Fuzzy processing, the fuzzy fact of generation are carried out for semiconductor manufacturing equipment real-time monitoring parameter；Inference machine is based on the fuzzy fact, and fuzzy reasoning is carried out by with interacting for fuzzy rule base, generates fault diagnosis result；Practical situations based on fault diagnosis result, carry out the rule intensity self study amendment towards fuzzy rule base.The present invention carries out fuzzy reasoning and then draws fault diagnosis result based on rete algorithms, and the rule intensity self study amendment of fuzzy rule base is fully carried out with reference to physical fault diagnostic application situation, the accurate level and automatization level of semiconductor manufacturing equipment fault diagnosis are improve, the efficient stable operation of semiconductor technology has been ensured.
Description
Technical field
The invention belongs to field of semiconductor manufacture, and in particular to a kind of fault diagnosis side towards semiconductor manufacturing equipment
Method.
Background technology
Production capacity influence of the running status of process equipment on enterprise is great in Semiconductor enterprises, and semiconductor technology sets
Standby cost is extremely expensive, and it is to improve enterprises' production capacity, control life to improve maintenance of equipment efficiency, reduce Stop-Time of Device Failure
Produce a kind of important channel of cost.Therefore, be that semiconductor manufacturing equipment provides convenient is practical, dependable performance fault diagnosis
As Semiconductor enterprises in the urgent need to, and the structure of equipment become increasingly complex also to fault diagnosis bring greatly challenge.
Intelligent Fault Diagnosis Technique opens new way for this requirement, and intelligent Fault Diagnosis Technique is applied into semiconductor manufacturing equipment
Field has value very high.
With continuing to develop for artificial intelligence technology, intelligent Fault Diagnosis Technique has come into a new stage, and
Expert system technology is achieved and is widely applied as a very active branch of artificial intelligence field.This method is independent of
In the Mathematical Modeling of system, the Heuristics that it is summarized according to domain expert makes inferences and judges that simulation human expert's determines
Plan process come solve those need human expert process challenge.As can be seen here, expert system technology is set up and is safeguarded and knows
Know storehouse, the mode of thinking for simulating the mankind carries out intelligent decision, can not only greatly improve the accuracy and reliability of fault diagnosis, and
And dependence to human expert's experience is reduced, improve the automaticity of fault diagnosis.
Current expert system uses forward reasoning mode mostly, when knowledge rule number is more, it is easy to send out
The explosivity of raw knowledge is combined and causes pattern match efficiency relatively low；On the other hand, in expert system fuzzy knowledge base structure
Expertise is depended on, is also to rely primarily on expert to carry out knowledge base during the use of fuzzy inference system
Safeguard, lack the self study correction mechanism based on the reasoning results sample.
The content of the invention
For above-mentioned technical deficiency, the present invention proposes a kind of method for diagnosing faults towards semiconductor manufacturing equipment, its mesh
Be：The redundancy of time of rete algorithms and structural similarity thought are dissolved into fuzzy reasoning, are used to overcome and are pushed away fuzzy
The relatively low problem of matching efficiency caused by the explosive combination of Li Shiyin knowledge, and then improve fuzzy reasoning efficiency；Build fuzzy
The self study correction mechanism of knowledge base, using in system day-to-day operation accumulate fault diagnosis sample, carry out rule intensity from
Whole fuzzy rule base is improved in study amendment, amendment, fuzzy reasoning is more had engineering practicability.
The technical scheme is that：A kind of method for diagnosing faults towards semiconductor manufacturing equipment, comprises the following steps：
1) Fuzzy processing, the fuzzy fact of generation are carried out for semiconductor equipment real-time monitoring parameter；
2) fuzzy rule base is set up；
3) the fuzzy fact is matched with the rule in fuzzy rule base using rete algorithms, is obtained fuzzy reasoning knot
Really；
4) fuzzy reasoning result is carried out into de-fuzzy and draws fault diagnosis result；
5) sample set is built according to fault diagnosis result and actual feedback result, rule intensity self-study is carried out based on sample set
Practise amendment.
Described carries out Fuzzy processing for semiconductor manufacturing equipment real-time monitoring parameter, is subordinate to using using Gauss
Degree function method, is realized by below equation：
Above formula is Gauss membership functions of the independent variable x for the monitoring parameter fuzzy collection A of semiconductor manufacturing equipment, and μ is
The Mean Parameters of monitoring of equipment parameter, σ is the variance parameter of monitoring of equipment parameter.
The fuzzy fact with the rule in fuzzy rule base match comprising the following steps by described use rete algorithms：
(1) rete fuzzy patterns are built：[PF] represents fuzzy rule former piece element, and P is parameter name, and F is fuzzy quantifier,
Then P and F are test domain, and P and F is connected with each other, and just constitute rete fuzzy patterns；
(2) rete connection networks are built：Connection net using in fuzzy rule with " and " relation rete fuzzy patterns as
One set, realizes the structure to fuzzy rule former piece, and each connection net sets a reteflag mark to record the connection
Whether the match is successful for net, if the match is successful, reteflag is true；Conversely, being then false；
(3) rete net mates：The fuzzy fact is matched with the test domain of rete fuzzy patterns, and by the match is successful
Fuzzy true storage in corresponding α registers, then the update status further according to α registers carry out reteflag more
Newly, it is the conclusion of the fuzzy rule of true using reteflag as fuzzy reasoning result.
Described is matched the fuzzy fact with the test domain of rete fuzzy patterns, and by the fuzzy fact that the match is successful
In storing corresponding α registers, then the update status further according to α registers carry out the renewal of reteflag includes following step
Suddenly：
It is the Ingress node of rete fuzzy pattern networks with parameter name test domain, proceeds by rete fuzzy pattern networks
Matching：
If the match is successful for parameter name test domain, find following fuzzy quantifier along matched chain and test domain node,
Proceed matching；
If all, the match is successful, by the fuzzy true storage of correspondence to α registers；
After the completion of rete fuzzy pattern net mate processes, just start to connect net mate：
After the α registers of certain fuzzy pattern are updated, the α registers of all patterns of traversal correspondence connection network,
And whether the match is successful to judge all patterns；If all the match is successful for the corresponding all patterns of the connection network, will
Reteflag is updated to true, using the conclusion of correspondence fuzzy rule as fuzzy reasoning result；If the connection network is corresponding
All the match is successful for all patterns, then reteflag is updated into false.
It is described rule intensity self study amendment is carried out based on sample set to comprise the following steps：
A. the error amount of the actual feedback result in sample set and result is calculated；
Whether B errors in judgement value exceeds the upper limit；
If without departing from the upper limit, amendment terminates；
Otherwise continue to determine whether beyond maximum iteration, if it was exceeded, amendment terminates；If without departing from entering
Row back-propagation, corrects the rule intensity of fuzzy rule, then recalculates the error amount of sample set；
C. return to step B, untill the error amount of sample set is reduced to higher limit or reaches maximum iteration.
The error amount is obtained by below equation：
In above formula, E is error amount, yIt is theoreticalIt is fault diagnosis result；yIt is actualIt is actual feedback result.
Described to carry out back-propagation, the rule intensity for correcting fuzzy rule is realized by following formula：
For the conclusion y of fuzzy reasoning resultj, the rule intensity w of corresponding certain the regular i of the conclusionijRegulation formula is as follows：
wij(n+1)=wij(n)+ηΔwijI=1,2 ..., M (3)
Wherein, Δ wijIt is the variable quantity of each rule intensity amendment, η is Learning Step, and n is current iteration number of times, and i is rule
Then sequence number, M is conclusion yjCorresponding fuzzy rules.
The variation delta w of each rule intensity amendmentijObtained by below equation：
Wherein, E is error amount, yIt is theoreticalIt is fault diagnosis result；yIt is actualIt is actual feedback, f (yj) it is conclusion yjCorresponding mould
Paste collection central value, yjIt is the corresponding decision confidence of conclusion, aijIt is conclusion yjThe former piece confidence level of corresponding regular i, wijIt is knot
By yjThe rule intensity of corresponding regular i.
The invention has the advantages that and advantage：
1. the present invention is obscured using Gauss membership function method when parameter fuzzy information is built to parameter
Change, with good antijamming capability, and obfuscation result is closer to the characteristics of cognition of the mankind.
2. fuzzy reasoning of the present invention based on rete algorithms is sufficiently used redundancy of time and structural similarity, saves
The memory space of fuzzy rule base, and improve Reasoning Efficiency so that fuzzy reasoning method can be suitably used for will to real-time
Seek engineering field higher.
3. the present invention corrects the rule intensity of fuzzy rule using BP algorithm, fully can diagnose sample based on fault routine
This, propagates to realize the amendment to rule intensity, and then make fuzzy rule base more conform to daily fortune using reverse error gradient
Market condition, improves the engineering practicability of expert system.
Brief description of the drawings
Fig. 1 is the structured flowchart of expert system of the invention.
Specific embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention
It is described in further detail.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and without
It is of the invention in limiting.
The present invention relates to a kind of method for diagnosing faults towards semiconductor manufacturing equipment, its job step is as follows：Step one,
The structure of parameter fuzzy information；The foundation of step 2, fuzzy rule base；Step 3, the fuzzy reasoning based on rete algorithms；Step
Rapid four, de-fuzzy and fault diagnosis result is drawn；Step 5, rule intensity self study amendment.With existing fuzzy reasoning side
Method is compared, and the present invention makes expert system be provided with preliminary self-learning capability by application rule intensity self study correction algorithm,
By the Reasoning Efficiency that expert system is improve using rete algorithms.Designed according to this invention expert system not only efficiency high,
Accuracy is high, and can be corrected based on routine use situation and improve fuzzy rule base, for engineer applied provide it is a kind of more
Plus practical and reliable method for diagnosing faults.
Present invention can apply to field of semiconductor manufacture, the failure towards semiconductor manufacturing equipment proposed by the present invention is examined
Disconnected method carries out software realization, then carries out software by carrier of the PC at semiconductor technology scene, server or industrial computer
Install, software is made inferences by input of the monitoring of equipment such as the light intensity of litho machine, luminous power parameter, realized by online mode
For the fault diagnosis of litho machine.
As shown in figure 1, technical scheme, specific design step is as follows：
Step one：The structure of parameter fuzzy information, specific method is：
The structure that litho machine monitors parameter fuzzy information is carried out first.Fuzzy introduction uses Gauss membership function
Method, it not only has good antijamming capability, and obfuscation result is closer to the characteristics of cognition of the mankind, its degree of membership
The expression formula of function is：
Above formula is Gauss membership functions of the independent variable x for the monitoring parameter fuzzy collection A of litho machine, and μ is supervised for litho machine
The Mean Parameters of parameter are surveyed, σ is the variance parameter that litho machine monitors parameter.
Step 2：The foundation of fuzzy rule base, specific method is：
The fuzzy rule of use represents method, and core concept is sought to traditional production rule " IF conditions THEN knots
By ", including condition obfuscation, conclusion obfuscation and Rules control, Fuzzy Production Rule can be by being expressed as below：
Rule:IFA1(f1,t1)andA2(f2,t2)and...andAn(fn,tn)THEN B(tB)CF
A1,A2,...,AnIt is knowledge former piece part, B is conclusion, they are litho machine monitoring parameter and fuzzy quantifier
Combination, we are represented with " PF ", and P is parameter name, for example light intensity, luminous power etc., and F is fuzzy quantifier, such as higher, relatively low
Deng f1,f2,...,fnBe the membership function of regular former piece, the parameter of membership function select be it is corresponding with its former piece,
t1,t2,...,tnThe former piece confidence level drawn after being calculated for membership function, tBIt is the confidence level of conclusion, CF is rule intensity, n
It is former piece number.
Step 3：Based on the fuzzy reasoning of rete algorithms, specific method is：
1) fuzzy reasoning based on rete algorithms
The litho machine monitoring parameter value that will be collected first carries out obfuscation and obtains the fuzzy fact, obscures true by ID, ginseng
Several titles, fuzzy quantifier and true confidence level are constituted, wherein true confidence level is to be input to degree of membership by by monitoring parameter value
Function is calculated.
Then the fuzzy fact is matched with the rule in fuzzy rule base using rete algorithms, is obtained fuzzy reasoning knot
Really, step is as follows：
(1) rete fuzzy patterns are built
Rete fuzzy patterns are connected in sequence by one or more test domains of fuzzy rule former piece element, each
Rete fuzzy patterns are owned by a α register, and the fact is successfully obscured with the pattern match for storing.
Fuzzy rule former piece element is represented with [PF], P is parameter name, and F is that fuzzy quantifier, then P and F are test
Domain, P and F is connected with each other, and just constitutes rete fuzzy patterns.
For certain rete fuzzy pattern, with α1As the register of the rete fuzzy patterns, when certain fuzzy true success
Have matched the P of the pattern1And F1During two test domains, then by the fuzzy true ID storages to α1In, while by the fuzzy mould
Formula is subordinate to angle value μ1It is entered as the fact that this is fuzzy true confidence level.
In order to avoid the structural redundancy of rete fuzzy patterns, and Reasoning Efficiency is improved, when rete fuzzy patterns are built,
Same test domain between each pattern can be shared.
(2) rete connection networks are built
After completing the structure of rete fuzzy patterns, then the foundation that rete connects network, rete connection networks pair are proceeded by
Should be in whole fuzzy rule base, it is made up of connection net, and each connection net corresponds to a fuzzy rule, therefore builds
The core of rete connection networks is the structure for connecting net.
Connection net using in fuzzy rule with " and " relation rete fuzzy patterns as one set, and then realize it is right
The structure of fuzzy rule former piece, each connection net can set a reteflag mark to record whether the connection net matches into
Work(, if the match is successful, reteflag is true；Conversely, being then false.
(3) rete net mates
Rete net mates are that the fuzzy fact is matched with the test domain of rete fuzzy patterns, and by the match is successful
The fuzzy fact is stored in corresponding α registers, and then the update status further according to α registers carry out the renewal of reteflag.
It is the Ingress node of rete fuzzy pattern networks with parameter name test domain, proceeds by rete fuzzy pattern networks
Matching.
If the match is successful for parameter name test domain, find following fuzzy quantifier along matched chain and test domain node,
Proceed matching；
If all, the match is successful, and correspondence is obscured into true ID stores in α registers.Due to that can be total between pattern
Identical test domain is enjoyed, therefore the matching efficiency of rete fuzzy patterns will be greatly improved.
After the completion of rete fuzzy pattern net mate processes, just start to connect net mate, mainly the α according to change is posted
Storage corresponds to reteflag to update, and embodies redundancy of time, and then improve Reasoning Efficiency.Its main flow is：When certain
After the α registers of individual fuzzy pattern are updated, the α registers of all patterns of traversal correspondence connection network, if certain pattern
α registers in have fuzzy fact ID, then pattern match success, otherwise pattern match failure is determined whether correspondingly
Whether the match is successful for all patterns of connection network；If all the match is successful for the corresponding all patterns of the connection network, will
Reteflag is updated to true, using the conclusion of correspondence fuzzy rule as fuzzy reasoning result；If the connection network is corresponding
All the match is successful for all patterns, then reteflag is updated into false.
In rete net mate processes, using Mamdani methods come the confidence level of composition rule former piece, for conclusion yjCorrespondence
Certain regular i, if it has n former piece subitem, between former piece subitem all with " and " connect, conclusion yjThe former piece of corresponding regular i
Confidence level composite formula is as follows：
WhereinFor each former piece subitem is subordinate to angle value in regular i, if the corresponding rule sums of conclusion j
It is M, conclusion yjThe rule intensity of corresponding regular i is wij, then the confidence level of conclusion j be：
Step 4：De-fuzzy simultaneously draws fault diagnosis result, and specific method is：
The system carries out de-fuzzy and processes and draw fault diagnosis result using gravity model appoach, and it takes full advantage of fuzzy pushing away
All information in reason result, the clear value for obtaining has good robustness.Computing formula is as follows：
Wherein N is the number of fuzzy reasoning result, f (yj) it is conclusion yjCorresponding fuzzy set central value, fuzzy set central value
Set according to practical situations.
Step 5：Rule intensity self study correction algorithm, specific method is：
The reverse error gradient that rule intensity self study amendment has incorporated BP (Back Propagation) algorithm propagates think of
Think, according to actual feedback result and the difference situation of fault diagnosis result, error to de-fuzzy and is drawn into reasoning knot successively
Fruit link, the fuzzy reasoning link based on rete algorithms are transmitted, and then realize the amendment to rule intensity, constantly instead
Multiple said process, until error convergence is to minimum value.
Rule intensity self study amendment flow is comprised the steps of：
A. the error amount of the actual feedback result in sample set and fault diagnosis result is calculated；
Whether B errors in judgement value exceeds the upper limit；
If without departing from the upper limit, amendment terminates；
Otherwise continue to determine whether beyond maximum iteration, if it was exceeded, amendment terminates；If without departing from entering
Row back-propagation, corrects the rule intensity of fuzzy rule, then recalculates the error total value of sample set；
C. return to step B, untill the error amount of sample set is reduced to higher limit or reaches maximum iteration.
The rule intensity self study correction algorithm of design uses batch processing mode, is with the correction algorithm of single sample below
Example illustrates its principle, and the amendment of batch processing sample is exactly adding up for single sample amendment, principle is similar.
The error function E of single sample conclusion is：
In above formula, yIt is theoreticalIt is fault diagnosis result；yIt is actualIt is actual feedback result.
For the conclusion y of fuzzy reasoning resultj, corresponding certain the regular i of the conclusion rule intensity regulation formula it is as follows：
wij(n+1)=wij(n)+ηΔwijI=1,2 ..., M (6)
In above formula, Δ wijIt is the variable quantity of each rule intensity amendment, η is Learning Step, and n is current iteration number of times, and i is
Number of regulation, M is conclusion yjCorresponding fuzzy rules.
Due to the system towards varying number level end value when, be modified using identical Learning Step, therefore be
The efficient learning efficiency of guarantee and preferably convergence effect, by the reasoning results value a common standard can be mapped to before amendment
In interval, therefore only need to set Learning Step η according to standard interval.
The quasi- interval of bidding is { std_down, std_up }, and wherein std_down is standard interval limit, and std_up is standard
The interval upper limit；If it is { obj_down, obj_up } that the codomain of current goal is interval, wherein obj_down is standard interval limit,
Obj_up is the standard interval upper limit, then mapping equation is as follows：
In above formula, obj_value is to be mapped to the numerical value before standard interval, after std_value is to be mapped to standard interval
Numerical value.
According to Feedback error algorithm, reverse error gradient is successively as shown in formula (8), formula (9).In formula (9), according to
Center-of-gravity defuzzifier formula, as N=1, by yjWith f (yj) merged, it is mainly in view of at following 2 points：
1) in view of f (yj) it is individual and yjRelated discrete function, it is impossible to derivation；
2)f(yj) the order of magnitude it is different, f (yj) inherently zooming in or out to a kind of constant amount of error.
Work as N>It is according to the feature of Center-of-gravity defuzzifier, each conclusion value is credible with conclusion with the difference of center-of-gravity value when 1
The result that degree summation is divided by approximately is transmitted as error gradient, so both take into account each conclusion value relative to center-of-gravity value
Bias factor, discrete function f (y are avoided againj) derivation problem, and confidence level is incorporated into consideration.
E is error amount, yIt is theoreticalIt is fault diagnosis result；yIt is actualIt is actual feedback, f (yj) it is conclusion yjCorresponding fuzzy set
Central value, yjIt is the corresponding decision confidence of conclusion, aijIt is conclusion yjThe former piece confidence level of corresponding regular i, wijIt is conclusion yj
The rule intensity of corresponding regular i.
As can be seen that the partial derivative in formula (8) is certainly existed.In formula (9), if denominatorIt is formula if zero
(9) partial derivative is non-existent, but in general, denominatorIt is y if zeroIt is theoreticalAlso it is just zero, illustrates that the conclusion is basic
It is not activated, it will not be also fed back, therefore the partial derivative of formula (9) is also what is existed.Due to f (yj) mark can be mapped to
Without being zero in quasi- interval, so the partial derivative in formula (10) is also certainly existed.
The workflow of the fault diagnosis expert system towards semiconductor manufacturing equipment that Fig. 1 is provided for the present invention, specifically
Method mainly includes following four step：
1) structure of parameter fuzzy information
By taking litho machine as an example, its monitoring parameter is light intensity and luminous power, is represented with A and B respectively, and fault diagnosis result is electricity
Road probability of damage, is represented with Y；
Set up the obfuscation information of parameter, the obfuscation information of light intensity and luminous power respectively as shown in table 1 to table 2, mainly
Setting including Mean Parameters and variance parameter in Gauss membership function, the obfuscation information such as table 3 of circuit probability of damage
It is shown, the corresponding central value of each fuzzy quantifier is mainly set；
The obfuscation information of the A of table 1
| It is low | In | It is high | |
| Mean Parameters | 100 | 150 | 200 |
| Variance parameter | 20.8 | 20.8 | 20.8 |
The obfuscation information of the B of table 2
| It is low | In | It is high | |
| Mean Parameters | 100 | 150 | 200 |
| Variance parameter | 20.8 | 20.8 | 20.8 |
The obfuscation information of the Y of table 3
| It is extremely low | It is relatively low | It is moderate | It is higher | It is high |
| 0.1 | 0.3 | 0.5 | 0.7 | 0.9 |
2) structure of fuzzy rule base
For litho machine, following three typical fuzzy rules, the former piece of fuzzy rule support " bracket ", " and " and
" or " three kinds of logical symbols, when the storage of fuzzy rule is carried out, can first solve bracket, then according to before " or " left and right two ends
Fuzzy rule is split as two rules by part；
ID1:Low high rule intensity=0.9 of THEN Y=of IF A=and B=high
ID2:Moderate rule intensity=0.8 of THEN Y=in or B=in IF A=
ID3:The low and B=of IF A=extremely low rule intensity=0.9 of THEN Y=high
3) fuzzy reasoning
Data based on initial input carry out fuzzy reasoning, then de-fuzzy and draw fault diagnosis result, initial defeated
Enter as shown in table 4 with fault diagnosis result；
The initial input of table 4 and fault diagnosis result
| Initial input A | Initial input B | The reasoning results Y |
| 200 | 32 | 0.814 |
| 160 | 40 | 0.708 |
| 140 | 43 | 0.546 |
As can be seen from Table 4, initial input and fault diagnosis result conform generally to the rule described by fuzzy rule, i.e. A
Ratio with B is higher, then Y is higher.
4) rule intensity self study amendment
When fault diagnosis reasoning result and actual feedback in table 4 are not inconsistent, actual feedback, such as table 5 can be submitted to
It is shown；
The contrast of the fault diagnosis result of table 5 and actual feedback
| Final the reasoning results | Actual feedback |
| 0.814 | 0.7 |
| 0.708 | 0.58 |
| 0.546 | 0.47 |
Then the difference according to final the reasoning results and actual feedback is come modification rule intensity, wherein, the rule of ID1 is strong
It is regular strong due to not being activated that degree has been adapted to 0.9996, ID3 by 0.9 rule intensity for being adapted to 0.15, ID2 by 0.9
Degree keeps constant, a fuzzy reasoning is then carried out again, as a result as shown in table 6；
The contrast of the revised fault diagnosis result of table 6 and desired output
| Revised final the reasoning results | Actual feedback |
| 0.7 | 0.7 |
| 0.579 | 0.58 |
| 0.508 | 0.47 |
As can be seen from Table 6, revised fault diagnosis result more closing to reality value of feedback, demonstrates rule intensity certainly
Study correction algorithm has preferable effect.
Claims (8)
1. a kind of method for diagnosing faults towards semiconductor manufacturing equipment, it is characterised in that including fuzzy rule base and inference machine,
Workflow is as follows：
1) Fuzzy processing, the fuzzy fact of generation are carried out for semiconductor manufacturing equipment real-time monitoring parameter；
2) fuzzy rule base is set up；
3) the fuzzy fact is matched with the rule in fuzzy rule base using rete algorithms, is obtained fuzzy reasoning result；
4) fuzzy reasoning result is carried out into de-fuzzy and draws fault diagnosis result；
5) sample set is built according to fault diagnosis result and actual feedback result, rule intensity self study is carried out based on sample set and is repaiied
Just.
2. the method for diagnosing faults towards semiconductor manufacturing equipment according to claim 1, it is characterised in that described to be directed to
Semiconductor manufacturing equipment real-time monitoring parameter carries out Fuzzy processing, is using Gauss membership function method is used, by following
Formula is realized：
Above formula is Gauss membership functions of the independent variable x for semiconductor manufacturing equipment monitoring parameter fuzzy collection A, and μ is Supervision
The Mean Parameters of parameter are surveyed, σ is the variance parameter of monitoring of equipment parameter.
3. the method for diagnosing faults towards semiconductor manufacturing equipment according to claim 1, it is characterised in that the use
The fuzzy fact with the rule in fuzzy rule base match comprising the following steps by rete algorithms：
Step 1：Build rete fuzzy patterns：[PF] represents fuzzy rule former piece element, and P is parameter name, and F is fuzzy quantifier,
Then P and F are test domain, and P and F is connected with each other, and just constitute rete fuzzy patterns；
Step 2：Build rete connection networks：Connection net using in fuzzy rule with " and " relation rete fuzzy patterns as
One set, realizes the structure to fuzzy rule former piece, and each connection net sets a reteflag mark to record the connection
Whether the match is successful for net, if the match is successful, reteflag is true；Conversely, being then false；
Step 3：Rete net mates：The fuzzy fact is matched with the test domain of rete fuzzy patterns, and by the match is successful
Fuzzy true storage in corresponding α registers, then the update status further according to α registers carry out reteflag more
Newly, it is the conclusion of the fuzzy rule of true using reteflag as fuzzy reasoning result.
4. the method for diagnosing faults towards semiconductor manufacturing equipment according to claim 3, it is characterised in that described by mould
The paste fact is matched with the test domain of rete fuzzy patterns, and the fuzzy true storage that the match is successful is deposited to corresponding α
In device, then the update status further according to α registers carry out the renewal of reteflag and comprise the following steps：
It is the Ingress node of rete fuzzy pattern networks with parameter name test domain, proceeds by rete fuzzy pattern networks
Match somebody with somebody：
If the match is successful for parameter name test domain, find following fuzzy quantifier along matched chain and test domain node, continue
Matched；
If all, the match is successful, by the fuzzy true storage of correspondence to α registers；
After the completion of rete fuzzy pattern net mate processes, just start to connect net mate：
After the α registers of certain fuzzy pattern are updated, the α registers of all patterns of traversal correspondence connection network, and sentence
Whether the match is successful for all patterns of breaking；If all the match is successful for the corresponding all patterns of the connection network, by reteflag
True is updated to, using the conclusion of correspondence fuzzy rule as fuzzy reasoning result；If the corresponding all patterns of the connection network
All the match is successful, then reteflag is updated into false.
5. the method for diagnosing faults towards semiconductor manufacturing equipment according to claim 1, it is characterised in that described to be based on
Sample set carries out rule intensity self study amendment and comprises the following steps：
Step 1：Calculate the error amount of the actual feedback result and fault diagnosis result in sample set；
Step 2：Whether error in judgement value exceeds the upper limit；
If without departing from the upper limit, amendment terminates；
Otherwise continue to determine whether beyond maximum iteration, if it was exceeded, amendment terminates；If without departing from carrying out anti-
To error propagation, the rule intensity of fuzzy rule is corrected, then recalculate the error amount of sample set；
Step 3：Return to step 2, untill the error amount of sample set is reduced to higher limit or reaches maximum iteration.
6. the method for diagnosing faults towards semiconductor manufacturing equipment according to claim 5, it is characterised in that the error
Value is obtained by below equation：
Wherein, E is error amount, yIt is theoreticalIt is fault diagnosis result；yIt is actualIt is actual feedback result.
7. the method for diagnosing faults towards semiconductor manufacturing equipment according to claim 5, it is characterised in that described to carry out
Back-propagation, the rule intensity for correcting fuzzy rule is realized by following formula：
For the conclusion y of fuzzy reasoning resultj, the rule intensity w of corresponding certain the regular i of the conclusionijRegulation formula is as follows：
wij(n+1)=wij(n)+ηΔwijI=1,2 ..., M (3)
Wherein, Δ wijIt is the variable quantity of each rule intensity amendment, η is Learning Step, and n is current iteration number of times, and i is regular sequence
Number, M is conclusion yjCorresponding fuzzy rules.
8. the method for diagnosing faults towards semiconductor manufacturing equipment according to claim 7, it is characterised in that it is described every time
The variation delta w of rule intensity amendmentijObtained by below equation：
Wherein, E is error amount, yIt is theoreticalIt is fault diagnosis result；yIt is actualIt is actual feedback, f (yj) it is conclusion yjCorresponding fuzzy set
Central value, yjIt is the corresponding decision confidence of conclusion, aijIt is conclusion yjThe former piece confidence level of corresponding regular i, wijIt is conclusion yj
The rule intensity of corresponding regular i.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710166248.9A CN106919982A (en) | 2017-03-20 | 2017-03-20 | A kind of method for diagnosing faults towards semiconductor manufacturing equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710166248.9A CN106919982A (en) | 2017-03-20 | 2017-03-20 | A kind of method for diagnosing faults towards semiconductor manufacturing equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106919982A true CN106919982A (en) | 2017-07-04 |
Family
ID=59460928
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710166248.9A Pending CN106919982A (en) | 2017-03-20 | 2017-03-20 | A kind of method for diagnosing faults towards semiconductor manufacturing equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106919982A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108416443A (en) * | 2018-03-29 | 2018-08-17 | 北京润科通用技术有限公司 | A kind of method for diagnosing faults and device |
| CN110245755A (en) * | 2019-06-20 | 2019-09-17 | 贵阳海信网络科技有限公司 | The mantenance data matching process and equipment of faulty equipment |
| CN110502779A (en) * | 2019-07-03 | 2019-11-26 | 广东工业大学 | A kind of Intelligentized design method of the injection forming mold based on fuzzy reasoning |
| CN110705812A (en) * | 2019-04-15 | 2020-01-17 | 中国石油大学(华东) | Industrial fault analysis system based on fuzzy neural network |
| CN111062480A (en) * | 2019-11-28 | 2020-04-24 | 上海宏力达信息技术股份有限公司 | Intelligent switch service life management method and system, storage medium and terminal |
| CN111832731A (en) * | 2020-07-09 | 2020-10-27 | 西安交通大学 | Multi-index monitoring method for representing uncertain state of oil and diagnosing faults |
| CN111832730A (en) * | 2020-07-09 | 2020-10-27 | 西安交通大学 | Reliability characterization and state identification method for uncertain oil state |
| CN115563549A (en) * | 2022-10-27 | 2023-01-03 | 武汉理工大学 | Welding defect cause diagnosis method and system and electronic equipment |
| CN115963723A (en) * | 2023-03-17 | 2023-04-14 | 深圳市鑫雅达机电工程有限公司 | Method for automatically adjusting and controlling operation of intelligent electromechanical system equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102662390A (en) * | 2012-04-26 | 2012-09-12 | 杭州电子科技大学 | Fault diagnosis method of random fuzzy fault characteristic fusion rotating mechanical device |
| US20120317058A1 (en) * | 2011-06-13 | 2012-12-13 | Abhulimen Kingsley E | Design of computer based risk and safety management system of complex production and multifunctional process facilities-application to fpso's |
| CN105321837A (en) * | 2014-06-23 | 2016-02-10 | 无锡华瑛微电子技术有限公司 | Semiconductor processing device and on-line fault detection method therefor |
| CN105787563A (en) * | 2014-12-18 | 2016-07-20 | 中国科学院沈阳自动化研究所 | Self-learning mechanism-base fast matching fuzzy reasoning method |
-
2017
- 2017-03-20 CN CN201710166248.9A patent/CN106919982A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120317058A1 (en) * | 2011-06-13 | 2012-12-13 | Abhulimen Kingsley E | Design of computer based risk and safety management system of complex production and multifunctional process facilities-application to fpso's |
| CN102662390A (en) * | 2012-04-26 | 2012-09-12 | 杭州电子科技大学 | Fault diagnosis method of random fuzzy fault characteristic fusion rotating mechanical device |
| CN105321837A (en) * | 2014-06-23 | 2016-02-10 | 无锡华瑛微电子技术有限公司 | Semiconductor processing device and on-line fault detection method therefor |
| CN105787563A (en) * | 2014-12-18 | 2016-07-20 | 中国科学院沈阳自动化研究所 | Self-learning mechanism-base fast matching fuzzy reasoning method |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108416443A (en) * | 2018-03-29 | 2018-08-17 | 北京润科通用技术有限公司 | A kind of method for diagnosing faults and device |
| CN110705812A (en) * | 2019-04-15 | 2020-01-17 | 中国石油大学(华东) | Industrial fault analysis system based on fuzzy neural network |
| CN110245755A (en) * | 2019-06-20 | 2019-09-17 | 贵阳海信网络科技有限公司 | The mantenance data matching process and equipment of faulty equipment |
| CN110502779A (en) * | 2019-07-03 | 2019-11-26 | 广东工业大学 | A kind of Intelligentized design method of the injection forming mold based on fuzzy reasoning |
| CN110502779B (en) * | 2019-07-03 | 2022-08-12 | 广东工业大学 | Intelligent design method of injection molding mold based on fuzzy reasoning |
| CN111062480A (en) * | 2019-11-28 | 2020-04-24 | 上海宏力达信息技术股份有限公司 | Intelligent switch service life management method and system, storage medium and terminal |
| CN111832731A (en) * | 2020-07-09 | 2020-10-27 | 西安交通大学 | Multi-index monitoring method for representing uncertain state of oil and diagnosing faults |
| CN111832730A (en) * | 2020-07-09 | 2020-10-27 | 西安交通大学 | Reliability characterization and state identification method for uncertain oil state |
| CN111832730B (en) * | 2020-07-09 | 2022-07-26 | 西安交通大学 | Reliability characterization and state identification method for uncertain oil state |
| CN115563549A (en) * | 2022-10-27 | 2023-01-03 | 武汉理工大学 | Welding defect cause diagnosis method and system and electronic equipment |
| CN115563549B (en) * | 2022-10-27 | 2023-10-20 | 武汉理工大学 | Welding defect cause diagnosis method and system and electronic equipment |
| CN115963723A (en) * | 2023-03-17 | 2023-04-14 | 深圳市鑫雅达机电工程有限公司 | Method for automatically adjusting and controlling operation of intelligent electromechanical system equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106919982A (en) | A kind of method for diagnosing faults towards semiconductor manufacturing equipment | |
| CN105787563B (en) | A kind of Rapid matching fuzzy reasoning method based on self-study mechanism | |
| CN108197648A (en) | A kind of Fault Diagnosis Method of Hydro-generating Unit and system based on LSTM deep learning models | |
| CN109783500A (en) | A kind of method for diagnosing faults based on fault tree and case similitude | |
| CN108090606A (en) | Equipment fault finds method and system | |
| CN109510201A (en) | A kind of distribution network reliability evaluation method based on Trapezoid Fuzzy Number system | |
| CN114006413A (en) | Transient stability control method and system for power system based on graph neural network | |
| Shi et al. | A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction | |
| CN114385403A (en) | Distributed cooperative fault diagnosis method based on double-layer knowledge graph framework | |
| CN117078123A (en) | Method and system for calculating available transmission capacity of electric-gas comprehensive energy system | |
| CN111144572A (en) | Power distribution network disaster situation inference method and system based on tree-shaped Bayesian network | |
| CN110489852A (en) | Improve the method and device of the wind power system quality of data | |
| CN110674893A (en) | Self-adaptive correction method for diagnosis experience in rotary machine fault diagnosis knowledge base | |
| Wei et al. | Application of rough set and neural network in water energy utilization | |
| CN116485361A (en) | Hydropower plant auxiliary equipment fault diagnosis method based on knowledge graph | |
| CN115996169A (en) | Network fault analysis method and device, electronic equipment and storage medium | |
| CN112801815B (en) | Power communication network fault early warning method based on federal learning | |
| CN108416443A (en) | A kind of method for diagnosing faults and device | |
| Chu et al. | A relaxed support vector data description algorithm based fault detection in distribution systems | |
| CN114662989A (en) | Transient stability self-adaptive evaluation method and device for power system | |
| CN114611590A (en) | Graph neural network-based power system missing data reconstruction method and system | |
| CN114548762A (en) | Real-time power system cascading failure risk assessment method and system based on space-time diagram neural network | |
| Lou et al. | Research on Diagnostic Reasoning of Cloud Data Center Based on Bayesian Network and Knowledge Graph | |
| Song et al. | A fast method for probabilistic reliability assessment of bulk power system using FSOM neural network as system states filters | |
| Iverson et al. | A diagnosis system using object-oriented fault tree models |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20171101 Address after: South Street of Shenhe District of Shenyang City, Liaoning Province, No. 114 110016 Applicant after: Shen Yang Automation Inst., Chinese Academy of Sciences Applicant after: China Electric Technology Group Chongqing acoustic photoelectric Co., Ltd. Address before: South Street in Dongling District of Shenyang city of Liaoning Province, No. 114 110016 Applicant before: Shen Yang Automation Inst., Chinese Academy of Sciences |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170704 |
|
| RJ01 | Rejection of invention patent application after publication |