CN109828622A - Diffusion furnace temprature control method and control system based on Wiener model control algolithm - Google Patents
Diffusion furnace temprature control method and control system based on Wiener model control algolithm Download PDFInfo
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- CN109828622A CN109828622A CN201910126057.9A CN201910126057A CN109828622A CN 109828622 A CN109828622 A CN 109828622A CN 201910126057 A CN201910126057 A CN 201910126057A CN 109828622 A CN109828622 A CN 109828622A
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Abstract
The invention discloses a kind of diffusion furnace temprature control methods and control system based on Wiener model control algolithm, and specific step is as follows for the temprature control method: (1) nonlinear Control object, that is, diffusion furnace heating system being described with Wiener model；(2) by the inverse operation to static non linear φ, the temperature control process of nonlinear Control is reduced to linear optimal contro8；(3) internal state variable of control object is simulated using state observer.Compared with prior art, diffusion method for controlling furnace temperature of the invention is easily programmed, the smaller suitable real-time control of calculation amount, be can be used as software and is run on microcomputer or computer and meets various demands, widely used.By verifying, control method of the invention is keeping temperature uniformity while can be realized to be rapidly heated, and compared with carrying out consumption electric power under heating rate identical as PID controller setting, saves about 3.68% electricity consumption than PID controller.
Description
Technical field
The present invention relates to the technical field of temperature control of integrated circuit manufacturing equipment diffusion furnace, and in particular to is based on wiener mould
The diffusion furnace temprature control method and control system of type control algolithm.
Background technique
Diffusion furnace is more common equipment in IC manufacturing, and operating temperature is higher, and generally greater than 1000 is Celsius
It spends, rapidity and the uniformity of in-furnace temperature are to judge the important indicator of diffusion furnace performance in temperature-rise period, the expansion haveing excellent performance
While scattered furnace needs satisfaction to be rapidly heated, the uniformity of in-furnace temperature is kept, so needing in diffusion furnace excellent using performance
Different temperature controller.
Since the temperature span in furnace body from low temperature to high temperature is larger, control object, that is, furnace body temperature shows very strong non-
Linearly, general control method is difficult to meet control requirement, in order to meet the requirement of temperature uniformity, can only reduce the speed of heating
Degree, the volume of diffusion furnace is larger in addition, needs to work at the same time using multiple groups PID controller, is easy to interfere with each other initiation temperature in this way
Degree concussion, influences using and causes the waste of electric power.
Summary of the invention
The diffusion furnace temperature control based on Wiener model control algolithm that technical problem to be solved by the invention is to provide a kind of
Method processed, the furnace body heating rate that it can solve diffusion furnace in the prior art is slow, heating uniformity difference problem.In addition, this
Invention also provides a kind of general temperature control system based on Wiener model control algolithm.
To solve the above-mentioned problems, the invention adopts the following technical scheme:
The first aspect of the present invention provides a kind of diffusion furnace temprature control method based on Wiener model control algolithm, this method
Realization steps are as follows:
(1) nonlinear Control object, that is, diffusion furnace heating system is described with Wiener model；
(2) by the inverse operation to static non linear φ, the temperature control process of nonlinear Control is reduced to linear optimal contro8；
(3) internal state variable of control object is simulated using state observer.
Wherein, after the step (3) further include: by control amount u, measured temperature z_k feed back to state observer into
Row state observation, the internal state x _ k for the linear dynamic part that state observer calculates and the output of linear dynamic part
Error e between estimated value y _ k, y _ k and control target value, optimal feedback control amount is calculated by error e, x _ k
u。
Wherein, Wiener model is obtained by following steps in the step (1):
(a) using the time constant of step response measurement control object, for designing input signal, i.e. M sequence；
(b) M sequence is input to control object, is tested, obtain temperature data；
(c) output signal of control object is acquired and is saved, constitute inputoutput data pair with corresponding input signal；
(d) above-mentioned inputoutput data pair is utilized, System Discrimination is carried out, calculates each parameter in Wiener model；
(e) Wiener model is constructed, with the linear dynamic part of state space description Wiener model, with polynomial approximation Wiener model
In static non linear part.
Wherein, the step (d) further includes the steps that verifying the model of foundation.
The second aspect of the present invention provides a kind of general control systems based on Wiener model control algolithm, for general non-
Linear Control object is described using above-mentioned Wiener model, System Discrimination modeling, so that it may construct one with linear optimal contro8
Based on control system: including nonlinear transformation module, feedback oscillator control module, power amplifier, the non-linear change
The output end of mold changing block connects the input terminal of the feedback oscillator control module, and the output end of the feedback oscillator control module connects
The input terminal of the power amplifier is connect, the output end of the power amplifier connects the input terminal of the control object, described
Nonlinear transformation module is used to be converted to setting value (such as temperature) controlling value of linear dynamic part.
It further, further include state observer, the input terminal of the state observer connects the feedback oscillator module
Output end and control object output end, the output end of the state observer connects the defeated of the feedback oscillator control module
Enter end.
Further, the state observer uses extended Kalman filter.
Compared with prior art, diffusion furnace temprature control method of the invention is easily programmed, and calculation amount is smaller to be suitble in real time
Control, can be used as software and runs on microcomputer or computer and meet various demands, widely used.By verifying, control of the invention
Algorithm is keeping temperature uniformity while can be realized to be rapidly heated, and disappears under heating rate identical as PID controller setting
Power-consuming power compares, and saves about 3.68% electricity consumption than PID controller.
Detailed description of the invention
Invention is further described in detail with specific embodiment with reference to the accompanying drawing.
Fig. 1 is the schematic diagram for the Wiener model that the present invention establishes；
Fig. 2 is that Wiener model of the present invention parameterizes schematic diagram；
Fig. 3 is the modeling procedure figure of Wiener model in diffusion furnace temprature control method of the present invention；
Fig. 4 is the structural schematic diagram of diffusion furnace temperature control system of the present invention；
Fig. 5 is the structural schematic diagram that the experimental facilities of temperature control experiment is carried out to diffusion furnace temprature control method of the present invention；
Fig. 6 is the heating curve figure of diffusion furnace temprature control method of the present invention and PID control method.
Specific embodiment
The present invention is directed to the climate control issues of diffusion furnace, right first using the strategy of the controller based on Wiener model
Control object (diffusion furnace) founding mathematical models, that is, Wiener model, then building is directed to the nonlinear control system of Wiener model,
The effect of the control system is verified in small-sized experimental facilities later.
The present invention is based on the diffusion furnace temprature control methods of Wiener model control algolithm, the specific steps of which are as follows:
The first step establishes Wiener model.
As shown in Figure 1, any time-invariant system of Wiener model that the present invention establishes can be stated by Wiener model, wiener mould
Type includes a linear dynamic part and a nonlinear static polymorphic segment (being labeled as φ), wherein u_k input, y_k are dynamic line
Property part output, be built-in variable, cannot directly measure, z_k be output, v_k be observation noise.By control object wiener
Model is stated, it is necessary to further be parameterized Wiener model, then with system identification theory come each ginseng in computation model
Number.It is contemplated that describing linear dynamic part with the state space in modern control theory, described with multinomial static non-
Linear segment parameterizes Wiener model.As shown in Fig. 2, extrapolating the parameters of Wiener model: shape using System Discrimination
(a_0, a_1, a_2 ...) in (A, B, C, the D) and multinomial φ of state space, thus can establish one can accurately retouch
State the mathematical model of control object.
Since the output of linear dynamic only differs a non-linear conversion with the output of entire control object, as long as passing through control
The output of linear dynamic part, the correspondingly output of available control object, such control strategy can simplify at present compared with
For mature Linear Control problem.When being applied to diffusion furnace temperature control, heater can be defined as a Wiener model,
The electric power input of heater corresponds to the input of Wiener model, and heater obtains the output that temperature exports corresponding Wiener model.
Modeling process uses step as shown in Figure 3:
(a) input signal designs；Using the time constant of step response measurement control object, input signal, i.e. M sequence are obtained.
The method that M sequence signal is designed by time constant can recognize textbook for example with frame of reference: author: towards dawn swallow, Meng Fanbin,
It is genuine " application of the M sequence in System Discrimination " to open book.
(b) loading experiment；M sequence is input to control object, is tested, temperature data is obtained, as the defeated of model
Signal out.
(c) DATA REASONING；The output signal of control object is acquired and is saved, is constituted with corresponding input signal defeated
Enter output data pair.
(d) System Discrimination is calculated；Using above-mentioned inputoutput data pair, determines model parameter and be stored in database.Make
With MATLAB software, a discreet value of each parameter is first calculated with state space recognition software packet therein, then uses parameter
Optimization software packet optimizes precompensation parameter, keeps model output and the error of actual measured value minimum, can be obtained by this way
The parameters of Wiener model.
(e) Wiener model constructs；Due to having extrapolated the parameters of Wiener model in System Discrimination, according to items
Parameter can construct the mathematical model that can accurately describe control object.
Second step constructs diffusion furnace temperature control system according to Wiener model.
As shown in figure 4, diffusion furnace temperature control system includes: nonlinear transformation module, feedback oscillator control module, power
Amplifier and state observer, the input terminal of the output end connection feedback oscillator control module of nonlinear transformation module, feedback increase
The input terminal of the output end connection power amplifier of beneficial control module, the output end connection control object (heating of power amplifier
Device) input terminal, nonlinear transformation module is for inputting set temperature value.The input terminal of state observer connects feedback oscillator mould
The temperature output end of the output end of block, heater, the input terminal of the output end connection feedback oscillator control module of state observer.
State observer uses extended Kalman filter.Power amplifier is using the product in existing market, the inverse fortune of static non linear
Calculation module, feedback oscillator control module are write using C language or MATLAB software, are loaded into computer or microcomputer circuit and are transported
Row.
The temperature-rise period of diffusion furnace is controlled using above-mentioned diffusion furnace temperature control system, the specific steps of which are as follows:
(1) set temperature is converted to the control of linear dynamic part by the inverse operation to multinomial φ by nonlinear transformation module
Target value processed；
(2) feedback oscillator controls；For feedback oscillator module by error e, x _ k calculates optimal control amount u；X _ k is shape
Intrinsic vector, the y _ k of the linear dynamic part that state observer calculates are the nonlinear Static that state observer calculates
Partial output, the error e difference that is state observation value y _ k between control target value.
(3) power amplifier is enlarged into current signal, voltage signal for target value is controlled, and obtains power output signal w.
(4) power output signal w is inputted into heater, obtains output signal, i.e. measured temperature z_k.
(5) control amount u, measured temperature z_k are fed back into state observer and carries out state observation, state observer calculates
Intrinsic vector x _ k of obtained linear dynamic part, output y _ k of nonlinear static polymorphic segment, state observation value y _ k and control
Error e between target value processed.
As shown in figure 5, carry out temperature control experiment using small-sized diffusion furnace, verify diffusion furnace temperature control system of the present invention and
The temperature-raising characteristic of PID control system.Wherein, diffusion furnace is three sections of heated types, and control circuit is three inputs, three output, temperature control target
1000 DEG C are warming up to from 600 DEG C as early as possible while three output temperature uniformities are to maintain less than 1 DEG C.Experiment effect is shown in Fig. 4.
As can be seen from Figure 6 compared with traditional PID control, present invention obtains faster temperature rise effects, while avoiding sending out
The concussion of raw temperature.Since power consumption is related with heating rate, in the limited situation of output power, identical heating speed is set
It carries out consumption electric power under rate to compare, control system of the present invention saves about 3.68% electricity consumption than PID controller, if it is existing
Product facility, temperature control is in the case where 4 to 5 sections, indeed it is contemplated that 5% to 10% power savings may be implemented.
Use above specific case is illustrated the present invention, is merely used to help understand the present invention, not to limit
The system present invention.For those skilled in the art, according to the thought of the present invention, can also make several simple
It deduces, deform or replaces.
Claims (6)
1. a kind of diffusion furnace temprature control method based on Wiener model control algolithm, which is characterized in that the realization of this method walks
It is rapid as follows:
(1) nonlinear Control object, that is, diffusion furnace heating system is described with Wiener model；
(2) by the inverse operation to static non linear φ, the temperature control process of nonlinear Control is reduced to linear optimal contro8；
(3) internal state variable of control object is simulated using state observer.
2. a kind of diffusion furnace temprature control method based on Wiener model control algolithm as described in claim 1, feature exist
In further comprising the steps of after the step (3): state estimation x _ k of e and state observer is optimal anti-to calculate
Feedback amount u.
3. a kind of diffusion furnace temprature control method based on Wiener model control algolithm as described in claim 1, feature exist
In Wiener model is obtained by following steps in the step (1):
(a) using the time constant of step response measurement control object, for designing input signal, i.e. M sequence；
(b) M sequence is input to control object, is tested, obtain temperature data；
(c) output signal of control object is acquired and is saved, constitute inputoutput data pair with corresponding input signal；
(d) above-mentioned inputoutput data pair is utilized, System Discrimination is carried out, calculates each parameter in Wiener model；
(e) Wiener model is constructed, with the linear dynamic part of state space description Wiener model, with polynomial approximation Wiener model
In static non linear part.
4. a kind of diffusion furnace temperature control system based on Wiener model control algolithm, using described in claim any one of 1-3
Temprature control method, which is characterized in that it is described including nonlinear transformation module, feedback oscillator control module, power amplifier
The output end of nonlinear transformation module connects the input terminal of the feedback oscillator control module, the feedback oscillator control module
Output end connects the input terminal of the power amplifier, and the output end of the power amplifier connects the input of the control object
End, the nonlinear transformation module are used to be converted to set temperature the control target value of linear dynamic part.
5. a kind of diffusion furnace temperature control system based on Wiener model control algolithm as claimed in claim 4, feature exist
In further including state observer, the input terminal of the state observer connects output end and the control of the feedback oscillator module
The output end of object, the output end of the state observer connect the input terminal of the feedback oscillator control module.
6. a kind of diffusion furnace temperature control system based on Wiener model control algolithm as claimed in claim 5, feature exist
In the state observer uses extended Kalman filter.
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| CN109828622B (en) * | 2019-02-20 | 2021-07-13 | 上海奕信半导体设备有限公司 | Diffusion furnace temperature control method and control system based on wiener model control algorithm |
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| CN106557627A (en) * | 2016-11-18 | 2017-04-05 | 南通大学 | recursive parameter estimation method based on state space Wiener model |
| CN107391442A (en) * | 2017-07-14 | 2017-11-24 | 西安交通大学 | A kind of augmentation linear model and its application process |
| CN107514916A (en) * | 2016-06-16 | 2017-12-26 | 许斌 | A kind of Control device of diffusion furnace and method |
| CN108914208A (en) * | 2018-07-23 | 2018-11-30 | 中国电子科技集团公司第四十八研究所 | A kind of diffusion furnace technology self diagnosis optimization method and device |
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| CN109828622B (en) * | 2019-02-20 | 2021-07-13 | 上海奕信半导体设备有限公司 | Diffusion furnace temperature control method and control system based on wiener model control algorithm |
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| CN107514916A (en) * | 2016-06-16 | 2017-12-26 | 许斌 | A kind of Control device of diffusion furnace and method |
| CN106557627A (en) * | 2016-11-18 | 2017-04-05 | 南通大学 | recursive parameter estimation method based on state space Wiener model |
| CN107391442A (en) * | 2017-07-14 | 2017-11-24 | 西安交通大学 | A kind of augmentation linear model and its application process |
| CN108914208A (en) * | 2018-07-23 | 2018-11-30 | 中国电子科技集团公司第四十八研究所 | A kind of diffusion furnace technology self diagnosis optimization method and device |
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| CN109828622B (en) * | 2019-02-20 | 2021-07-13 | 上海奕信半导体设备有限公司 | Diffusion furnace temperature control method and control system based on wiener model control algorithm |
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