CN102795627A - Multi-parameter online monitoring and optimizing control device and method of polycrystalline silicon reduction furnace - Google Patents

Abstract

The invention discloses a multi-parameter online monitoring and optimizing control device and method of a polycrystalline silicon reduction furnace. The device comprises a multi-parameter infrared monitoring probe, an infrared image processing and vision measuring module and a process optimizing control module, wherein the multi-parameter infrared monitoring probe obtains an infrared image of a silicon rod in the furnace; the infrared image is collected by a data collecting module and is converted into a digital image; the digital image is analyzed and processed by an image processing module, and the diameter and the growing rate of the silicon rod are obtained through a vision measuring technology; a colorimetric temperature measurement method is used for measuring temperature distribution on the surface of the silicon rod and obtained data is accessed to a display through a user interface; and an optimizing control module is established through measured data obtained by analyzing, and different polycrystalline silicon reduction furnace types are combined to carry out closed ring optimizing control. According to the multi-parameter online monitoring and optimizing control device and method, a silicon rod growing process can be optimally controlled; and the device and the method are of great importance of saving energy and reducing consumption, improving the production efficiency, guaranteeing the production safety and reducing the labor intensity.

Description

Polycrystalline silicon reducing furnace many reference amounts on-line monitoring and optimal control device and method

Technical field

The present invention relates to Si reduction stove production field, be specifically related to a kind of polycrystalline silicon reducing furnace many reference amounts on-line monitoring and optimal control device and method.

Technical background

Polysilicon is the basic material of electronic industry and solar energy industry, is widely used in semi-conductor chip, high-performance sensors, optical fiber, solar panel etc.To 2010, global polysilicon output reached 120,000 tons, and China has accounted for 50% of ultimate production, nearly 40,000,000,000 Renminbi of the output value.Estimate 2011, global polysilicon output will reach 160,000 tons, and Chinese proportion then will be brought up to more than 60% of ultimate production.

Parameters such as the temperature distribution on silicon rod surface, rod footpath, growth rate are the key links that improves the silicon rod growth quality in the reduction furnace.In the polycrystalline silicon rod process of growth, increase in temperature, the sedimentation rate of silicon on the silicon core increases, and the silicon rod growth is accelerated, but power consumption is too much, and temperature is high more, and gas phase condition is harsher, and the homogeneity of silicon rod is poor more; Temperature reduces, and the sedimentation rate of silicon is slack-off, and when temperature was lower than certain value, silicon rod ruptured easily, and what influence was grown proceeds.In addition, the diameter of silicon rod is the important evidence of temperature regulation and feed gas set of dispense ratio over time, and irrational proportioning can make the silicon rod growth velocity descend the mass consumption energy.Therefore, just press for a kind of can be in reduction furnace these parameters of on-line monitoring and the equipment that carries out closed-loop optimization control.

But the test of the on-line measurement of silicon rod many reference amounts and theoretical investigation that optimal control is all carried out and exploration does not have sophisticated plant and instrument and gets into production application at present both at home and abroad to reduction furnace.In the world, even all there is not the equipment of sophisticated on-line monitoring and control in the Siemens that is in the technological precedence status aspect the production of polysilicon technology controlling and process, Mitsubishi Electric Co. etc. yet; Blank especially at home, at present, the on-line monitoring and the optimal control of the many reference amounts (like silicon rod diameter, temperature etc.) in the reduction furnace rest on theoretical research stage basically.

So far; In the actual production of the whole nation over thousands of seat polycrystalline silicon reducing furnace; What adopt all is the open loop type master modes that are the basis with preset empirical curve, and temperature is by the some thermometric through observation window, fans out from point to area; And the measurement of diameter leans on the experience range estimation of workman's work to estimate that roughly obvious this method has certain subjectivity and randomness fully.

Given this, developed a kind of equipment that can in reduction furnace, carry out on-line measurement and carry out closed-loop optimization control to parameters such as the temperature distribution on silicon rod surface, excellent footpath, growth rates.

Summary of the invention

The invention solves the many reference amounts monitoring in real time of polycrystalline silicon growth process and the technical barrier of optimal control, the equipment of having developed the online accurate measurement of parameters such as a kind of silicon rod surface temperature distribution, silicon rod diameter, growth rate and having carried out closed-loop optimization control.To save energy and reduce the cost, enhance productivity, ensure production safety, reducing labor intensity all has very important meaning.

The technical scheme that the present invention adopts is:

Polycrystalline silicon reducing furnace many reference amounts on-line monitoring and optimal control device; It is characterized in that; Include many reference amounts infrared monitoring probe, infrared image processing and vision measurement module, process optimization control module; Described many reference amounts infrared monitoring probe includes near-infrared optical system, image capture module; The water-cooled shield cap is equipped with in many reference amounts infrared monitoring probe outside, and the front end of water-cooled shield cap is provided with water-cooling type and seals withstand voltage observation window, and many reference amounts infrared monitoring probe is deep in the polycrystalline silicon reducing furnace; Described infrared image processing and vision measurement module include infrared image processing module and vision measurement module; Data acquisition module, image processing module that the infrared image processing module links to each other successively; The vision measurement module includes color comparison temperature measurement module, diameter measurement module; The output terminal of image processing module is connected with color comparison temperature measurement module, diameter measurement module respectively, and the output terminal of color comparison temperature measurement module, diameter measurement module all inserts data output interface, user interface, and data output interface is circumscribed with the process optimization control module; The external indicating meter of user interface, the process optimization control module is connected with former system through a data interface.

Described process optimization control module is a closed-loop optimization control model.

Described image capture module is the infrared CCD camera.

The method of polycrystalline silicon reducing furnace many reference amounts on-line monitoring and optimal control is characterized in that, comprises following concrete steps:

1) many reference amounts infrared monitoring probe is through choosing optimum infrared band and highly sensitive infrared CCD camera; Obtain the picture signal of silicon rod growth in the stove through near-infrared optical system, infrared CCD camera; And carry out A/D through data acquisition module and change, obtain the infrared digital image of polysilicon rod;

2) infrared digital image of above-mentioned acquisition is sent into image processing module; And through image processing module carry out the image pre-treatment with cut apart; In conjunction with concrete image,, protected imaging surface temperature distribution characteristic to greatest extent through image denoising algorithm and level and smooth filtering algorithm; Find out the edge of sealing through carrying out the profile tracking, and carry out sub-pixel rim detection and fitting of a straight line, obtain the image border of degree of precision;

3) above-mentioned processed images is transported in the diameter measurement module of vision measurement module; The demarcation of at first carrying out the infrared CCD camera obtains the inside and outside parameter of camera; Carry out choosing of primary objective image border according to Hough fitting of a straight line algorithm, then image is carried out the tracking at edge, zonule; , the fundamental matrix that robustness is estimated carries out corresponding point matching under instructing; At last, pixel value and the effective unit system yardstick measured are converted, obtain the real-time diameter data of silicon rod according to above algorithm;

4) image of handling in the step 2 is transported in the color comparison temperature measurement module; Utilize the image processing system in the color comparison temperature measurement module that image is gone bad correction a little; To image cut apart, smoothly, layering, gradation conversion, call DB then, grey scale curve is carried out match; Temperature is demarcated, accomplish thermometric;

5) with the silicon rod diameter and the surface temperature data that obtain in the step 3,4, be transported in the process optimization control module, set up optimizing control models, instruct silicon rod under reasonable process conditions, to grow with feedback in conjunction with work information through data output interface;

6) silicon rod diameter and the surface temperature data with above-mentioned acquisition output to the terminal, obtain the real-time diameter and the growth rate of silicon rod, and the real time temperature of silicon rod surface any point, and in indicating meter, show.

Principle of work of the present invention is:

The present invention obtains silicon rod infrared image in the stove through many reference amounts infrared monitoring probe (NICCD); Analyze, handle by image processing module again through data collecting module collected, after converting digital picture into; Obtain silicon rod diameter and growth rate through the vision measurement technology; Adopt color comparison temperature measurement method to measure the temperature distribution on silicon rod surface; The gained data insert the take off data that indicating meter obtains through analysis through user interface, set up optimizing control models, carry out closed-loop optimization control in conjunction with different polycrystalline silicon reducing furnace types.

Beneficial effect of the present invention is:

1) the present invention compares with existing temperature measuring equipment and has that temperature-measuring range is big, precision is high, advantages such as long-term continuously measured;

2) the present invention compares with present artificial experience estimation silicon rod diameter and growth rate, and having can be continuously, the accurate advantage of measurement;

3) the present invention compares with the existing fixed master mode, has to be directed against the different type of furnaces and operating mode, and the advantage of corresponding closed-loop optimizing control models is provided.

Description of drawings

Fig. 1 is a system architecture functional block diagram of the present invention.

Fig. 2 optimizing control models synoptic diagram of the present invention,

Fig. 3 is a surface temperature monitoring result synoptic diagram in the silicon rod process of growth.

Fig. 4 is a silicon rod diameter monitoring result synoptic diagram in the silicon rod process of growth.

Embodiment

When doing furtherly to the present invention, but should not limit protection scope of the present invention with this below in conjunction with instance and accompanying drawing.

Structure of the present invention is formed as shown in Figure 1.

Polycrystalline silicon reducing furnace many reference amounts on-line monitoring and optimal control device; It is characterized in that; Include many reference amounts infrared monitoring probe 1, infrared image processing and vision measurement module 2, process optimization control module 3; Many reference amounts infrared monitoring probe 1 includes near-infrared optical system 4, image capture module 5; The water-cooled shield cap is equipped with in many reference amounts infrared monitoring probe outside, and the front end of water-cooled shield cap is provided with water-cooling type and seals withstand voltage observation window, and many reference amounts infrared monitoring probe is deep in the polycrystalline silicon reducing furnace; Infrared image processing and vision measurement module 2 include infrared image processing module and vision measurement module; Data acquisition module 6, image processing module 7 that the infrared image processing module links to each other successively; The vision measurement module includes color comparison temperature measurement module 8, diameter measurement module 9; The output terminal of image processing module 7 is connected with color comparison temperature measurement module 8, diameter measurement module 9 respectively; The output terminal of color comparison temperature measurement module 8, diameter measurement module 9 all inserts data output interface 12, user interface 10; Data output interface is circumscribed with process optimization control module 3, the external indicating meter 11 of user interface, and process optimization control module 3 is connected with former system through a data interface.

Process optimization control module 3 is closed-loop optimization control model.

Image capture module 5 is the infrared CCD camera.

The method of polycrystalline silicon reducing furnace many reference amounts on-line monitoring and optimal control comprises following concrete steps:

1) many reference amounts infrared monitoring probe 1 is through choosing optimum infrared band and highly sensitive infrared CCD camera; Obtain the picture signal of silicon rod growth in the stove through near-infrared optical system 4, infrared CCD camera; And carry out A/D through data acquisition module 6 and change, obtain the infrared digital image of polysilicon rod;

2) infrared digital image of above-mentioned acquisition is sent into image processing module 7; And through image processing module 7 carry out the image pre-treatment with cut apart; In conjunction with concrete image,, protected imaging surface temperature distribution characteristic to greatest extent through image denoising algorithm and level and smooth filtering algorithm; Find out the edge of sealing through carrying out the profile tracking, and carry out sub-pixel rim detection and fitting of a straight line, obtain the image border of degree of precision;

3) above-mentioned processed images is transported in the diameter measurement module 9 of vision measurement module; The demarcation of at first carrying out the infrared CCD camera obtains the inside and outside parameter of camera; Carry out choosing of primary objective image border according to Hough fitting of a straight line algorithm, then image is carried out the tracking at edge, zonule; , the fundamental matrix that robustness is estimated carries out corresponding point matching under instructing; At last, pixel value and the effective unit system yardstick measured are converted, obtain the real-time diameter data of silicon rod according to above algorithm;

4) image of handling in the step 2 is transported in the color comparison temperature measurement module 8; Utilize the image processing system in the color comparison temperature measurement module that image is gone bad correction a little; To image cut apart, smoothly, layering, gradation conversion, call DB then, grey scale curve is carried out match; Temperature is demarcated, accomplish thermometric;

5) with the silicon rod diameter and the surface temperature data that obtain in the step 3,4, be transported in the process optimization control module 3, set up optimizing control models, instruct silicon rod under reasonable process conditions, to grow with feedback in conjunction with work information through data output interface;

6) silicon rod diameter and the surface temperature data with above-mentioned acquisition output to the terminal, obtain the real-time diameter and the growth rate of silicon rod, and the real time temperature of silicon rod surface any point, and in indicating meter 11, show.

Inventive principle of the present invention is following:

In the vision measurement module, according to the real-time growth diameter of the captured silicon rod growth image amount of measuring silicon rod.

(1) demarcation of camera system

The pixel value that will in picture processing, obtain is converted into effective unit system scale-value, needs calibration system, promptly confirms how much shooting models of camera.Known spatial target three-dimensional point homogeneous coordinates are

; Its corresponding two-dimentional picture point homogeneous coordinates are , and the projective rejection between spatial point

and the picture point does

$s\stackrel{?}{m}=A\left[\begin{array}{cc}R& t\end{array}\right]\stackrel{?}{M}$

Utilize least square solution overdetermination system of linear equations, provide external parameter; Find the solution inner parameter,, can solve,, then find the solution through the optimization searching of a ternary if there is radial distortion by an overdetermination linear equation if pick up camera does not have lens distortion.Finally solve inside and outside parameter, focal distance f, coefficient of radial distortion k, rotation matrix R and translation vector T.

(2) rim detection and Hough fitting of a straight line

Rim detection utilizes single order and second dervative to detect based on the discontinuity of sensed luminance value.(x, gradient y) is defined as vector to two-dimensional function f

$\▿f=\left[\begin{array}{c}{G}_x\\ G_y\end{array}\right]=\left[\begin{array}{c}\∂f/\∂x\\ \∂f/\∂y\end{array}\right]$

This vectorial amplitude is

$\▿f=mag(\▿f)={[G_x^2+G_y^2]}^{1/2}={[{(\∂f/\∂x)}^2+{(\∂f/\∂y)}^2]}^{1/2}$

Generally be reduced to

$\&dtri;f=G_x^2+{\mathrm{<figure-callout\; id="2"\; label="G\; y"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">G</figure-callout>}}_y^{}$ Or $\&dtri;f=\left|G_x\right|+\left|G_y\right|$

Their values in constant brightness region are zero.

Use Hough conversion carrying out line to detect and link and at first will do the peak value detection; Find and include peaked Hough converter unit; Hough converter unit in the peak neighborhood of a point that finds is made as 0, repeats this step, till finding the peak value that needs.For each peak value, find the synthetic line segment of the relevant pixel groups in position.

(3) image corresponding point matching

Above-mentioned detected unique point is mated, promptly find out the picture point in the different images of corresponding the same space point.

Suppose to take two width of cloth pictures for the silicon rod of synchronization; The characteristic of correspondence point set is

in two width of cloth images; X (x, y, 1), x ' (x '; Y ', 1) representes the homogeneous coordinates of the corresponding points of left and right pick up camera respectively.Definition by fundamental matrix can know,

$\stackrel{\&RightArrow;}{{x^{\&prime;}}^T}F\stackrel{\&RightArrow;}{x}=0$ Promptly $\stackrel{\&RightArrow;}{A}\stackrel{\&RightArrow;}{f}=0$

Wherein, $\stackrel{\&RightArrow;}{f}={({\mathrm{<figure-callout\; id="11"\; label="F"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">F</figure-callout>}}_{11},{\mathrm{<figure-callout\; id="12"\; label="F"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">F</figure-callout>}}_{12},F_{13},F_{21},F_{22},F_{23},F_{31},F_{32},F_{33})}^T$ , satisfy constraint || f||=1;

$A=\left[\begin{array}{ccccccccc}{x_1}^{\&prime;}{x}_1& {x_1}^{\&prime;}{y}_1& {x_1}^{\&prime;}& {{\mathrm{<figure-callout\; id="1"\; label="y"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">y</figure-callout>}}_1}^{\&prime;}{x}_1& {{\mathrm{<figure-callout\; id="1"\; label="y"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">y</figure-callout>}}_1}^{\&prime;}{y}_1& {{\mathrm{<figure-callout\; id="1"\; label="y"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">y</figure-callout>}}_1}^{\&prime;}& x_1& {\mathrm{<figure-callout\; id="1"\; label="y"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">y</figure-callout>}}_1& 1\\ \&CenterDot;\&CenterDot;\&CenterDot;& \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;& \&CenterDot;\&CenterDot;\&CenterDot;& \&CenterDot;\&CenterDot;\&CenterDot;& \&CenterDot;\&CenterDot;\&CenterDot;& \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;& \&CenterDot;\&CenterDot;\&CenterDot;& \&CenterDot;\&CenterDot;\&CenterDot;& \&CenterDot;\&CenterDot;\&CenterDot;\\ x_n^{\&prime;}{x}_n& x_n^{\&prime;}{y}_n& x_n^{\&prime;}& y_n^{\&prime;}{x}_n& y_n^{\&prime;}{y}_n& y_n^{\&prime;}& x_n& y_n& 1\end{array}\right]$

After obtaining fundamental matrix and three-dimensional accurately matching point with the robustness algorithm for estimating,, fundamental matrix carries out more corresponding point matching under instructing.

(4) foundation of optimizing control models

Through the process variable that measurement is obtained, combine work information to set up optimizing control models again, instruct silicon rod growth rationally to carry out, reduce cost, improve the quality of products to reach and the purpose of energy-saving and emission-reduction

The color comparison temperature measurement method of color comparison temperature measurement system is following:

Thermodynamic temperature is the general object of T, and its radiation and distribution are described by the Planck radiation law:

$M(T,\mathrm{\&lambda;})=\mathrm{\&epsiv;}\left(\mathrm{\&lambda;}\right){\mathrm{<figure-callout\; id="1"\; label="c"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">c</figure-callout>}}_1/{\mathrm{\&lambda;}}^5[\exp \left(\frac{{\mathrm{<figure-callout\; id="2"\; label="c"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">c</figure-callout>}}_2}{\mathrm{\&lambda;T}}\right)-1]---\left(7\right)$

In the formula, M _{E λ}Be spectrum spoke out-degree, wherein λ is a wavelength, and T is a thermodynamic temperature, C ₁=3.741832 * 10 ^-12Wcm ²Be first radiation constant, C ₂=1.438786 * 10 ⁴μ mK is a second radiation constant; Work as c ₂/ λ T＞＞1 o'clock, planck formula can be replaced by Wien equation, can be reduced to:

$M(T,\mathrm{\&lambda;})=\mathrm{\&epsiv;}(\mathrm{\&lambda;}){\mathrm{<figure-callout\; id="1"\; label="c"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">c</figure-callout>}}_1/{\mathrm{\&lambda;}}^5\exp (\frac{{\mathrm{<figure-callout\; id="2"\; label="c"\; filenames="DEST\_PATH\_HDA00002148502600011.png"\; state="\{\{state\}\}">c</figure-callout>}}_2}{\mathrm{\&lambda;}T})---\left(8\right)$

< in the 2698 μ mK zones, the error of Wien equation and planck formula is less than 1% at λ T;

If two wavelength X ₁And λ ₂Measure the spectrum spoke out-degree M (T, the λ that send by the object same point simultaneously down, ₁) and M (T, λ ₂), then can this temperature according to both ratio, formula is:

$T=\frac{c_2(\frac{1}{{\mathrm{\&lambda;}}_2}?\frac{1}{{\mathrm{\&lambda;}}_1})}{\ln \frac{M(T,{\mathrm{\&lambda;}}_1)}{M(T,{\mathrm{\&lambda;}}_2)}?\ln \frac{\mathrm{\&epsiv;}(T,{\mathrm{\&lambda;}}_1)}{\mathrm{\&epsiv;}(T,{\mathrm{\&lambda;}}_2)}?5\ln \frac{{\mathrm{\&lambda;}}_2}{{\mathrm{\&lambda;}}_1}}---\left(9\right)$

Suppose ; Be proximately to be used as grey body to object and to handle, then formula (9) can be reduced to:

$T=\frac{c_2(\frac{1}{{\mathrm{\&lambda;}}_2}?\frac{1}{{\mathrm{\&lambda;}}_1})}{\ln \frac{M(T,{\mathrm{\&lambda;}}_1)}{M(T,{\mathrm{\&lambda;}}_2)}?5\ln \frac{{\mathrm{\&lambda;}}_2}{{\mathrm{\&lambda;}}_1}}----\left(10\right)$

This is the calculation formula of two-color thermometry.

Claims (4)

1. polycrystalline silicon reducing furnace many reference amounts on-line monitoring and optimal control device; It is characterized in that; Include many reference amounts infrared monitoring probe, infrared image processing and vision measurement module, process optimization control module; Described many reference amounts infrared monitoring probe includes near-infrared optical system, image capture module; The water-cooled shield cap is equipped with in many reference amounts infrared monitoring probe outside, and the front end of water-cooled shield cap is provided with water-cooling type and seals withstand voltage observation window, and many reference amounts infrared monitoring probe is deep in the polycrystalline silicon reducing furnace; Described infrared image processing and vision measurement module include infrared image processing module and vision measurement module; Data acquisition module, image processing module that the infrared image processing module links to each other successively; The vision measurement module includes color comparison temperature measurement module, diameter measurement module; The output terminal of image processing module is connected with color comparison temperature measurement module, diameter measurement module respectively, and the output terminal of color comparison temperature measurement module, diameter measurement module all inserts data output interface, user interface, and data output interface is circumscribed with the process optimization control module; The external indicating meter of user interface, the process optimization control module is connected with former system through a data interface.

2. polycrystalline silicon reducing furnace many reference amounts on-line monitoring according to claim 1 and optimal control device is characterized in that, described process optimization control module is a closed-loop optimization control model.

3. polycrystalline silicon reducing furnace many reference amounts on-line monitoring according to claim 1 and optimal control device is characterized in that, described image capture module is the infrared CCD camera.

4. the method for polycrystalline silicon reducing furnace many reference amounts on-line monitoring and optimal control is characterized in that, comprises following concrete steps:

Many reference amounts infrared monitoring probe is through choosing optimum infrared band and highly sensitive infrared CCD camera; Obtain the picture signal of silicon rod growth in the stove through near-infrared optical system, infrared CCD camera; And carry out A/D through data acquisition module and change, obtain the infrared digital image of polysilicon rod;

2) infrared digital image of above-mentioned acquisition is sent into image processing module; And through image processing module carry out the image pre-treatment with cut apart; In conjunction with concrete image,, protected imaging surface temperature distribution characteristic to greatest extent through image denoising algorithm and level and smooth filtering algorithm; Find out the edge of sealing through carrying out the profile tracking, and carry out sub-pixel rim detection and fitting of a straight line, obtain the image border of degree of precision;

3) above-mentioned processed images is transported in the diameter measurement module of vision measurement module; The demarcation of at first carrying out the infrared CCD camera obtains the inside and outside parameter of camera; Carry out choosing of primary objective image border according to Hough fitting of a straight line algorithm, then image is carried out the tracking at edge, zonule; , the fundamental matrix that robustness is estimated carries out corresponding point matching under instructing; At last, pixel value and the effective unit system yardstick measured are converted, obtain the real-time diameter data of silicon rod according to above algorithm;

4) image of handling in the step 2 is transported in the color comparison temperature measurement module; Utilize the image processing system in the color comparison temperature measurement module that image is gone bad correction a little; To image cut apart, smoothly, layering, gradation conversion, call DB then, grey scale curve is carried out match; Temperature is demarcated, accomplish thermometric;

5) with the silicon rod diameter and the surface temperature data that obtain in the step 3,4, be transported in the process optimization control module, set up optimizing control models, instruct silicon rod under reasonable process conditions, to grow with feedback in conjunction with work information through data output interface;

6) silicon rod diameter and the surface temperature data with above-mentioned acquisition output to the terminal, obtain the real-time diameter and the growth rate of silicon rod, and the real time temperature of silicon rod surface any point, and in indicating meter, show.

Images