**2.2 Cloud system and big data analysis technology**

This research is based on the fact that FTIR operates continuously for 24 hr and captures signals every second to obtain data throughout the year as a basis for big data. The main research significance is not to grasp the huge data information, but to professionally process these meaningful data, so that FAB factory managers can know whether the exhaust emissions meet the alert or warning potential under the sensing trend. Most of the research uses the big data platform for deployment, debugging and maintenance. This study is connected to the Chief Cloud eXchange (CCX) cloud database platform and the Spark big data platform. At the same time, these two platforms are also more suitable for primary systems to implement systems. **Figure 6** is the research cloud computing system and database architecture.

For the big data of the chip process exhaust obtained by RFID, full consideration should be given to (1) big data life cycle, (2) big data technology ecology, (3) big data acquisition and preprocessing, (4) big data storage and management, (5) Big data computing model and system.

The big data analysis method used in this study is described as follows. U is defined as the non-empty initial universe of the object. Then define E as a set of parameters related to the object in U. Let P (U) be the power set of U, and A ⊂ E. A pair (F, A) is called a soft set on U, where F is the mapping given by a F: A ! (U). In other words, the soft set on U is a parameterized family of Universe U subsets.

In addition, if the universe set U is a non-empty finite set, and σ is the equivalent relationship on U. Then (U, σ) is called approximate space. If X is a subset of U, then X can be written as a union of equivalent classes of U or not. If X can be written as a union of equivalent classes of U, then X is definable, otherwise it is undefinable. If X is undefinable, it can be approximated as two definable subsets, called the upper and lower approximation of X, as shown below Eq. (6) [22, 23].

$$\underline{\operatorname{app}}(X) = \cup \{ [\chi]\_{\sigma} : [\chi]\_{\sigma} \subseteq X \},$$

$$\overline{\operatorname{app}}(X) = \cup \{ [\chi]\_{\sigma} : [\chi]\_{\sigma} \cap X \neq \mathcal{O} \}. \tag{6}$$

**Figure 5.** *The absorption spectrum of several gas molecules in the infrared range.*

intensity of the light beam in this wavelength band is weakened, and the ratio of light intensity before and after absorption is The concentration of the gas is directly related. The absorption band and intensity of the gas sample can be measured to know the composition and concentration contained in the gas. For a maximum path difference d adjacent wavelengths λ1 and λ2 will have n and (n + 1) cycles respectively in the interferogram. The corresponding frequencies are ν1 and ν2, and the

FTIR mainly emits a beam of light to the measurement area and measures the intensity change of the beam after passing the gas to be measured. Since each gas molecule has its specific infrared light absorption coefficient, when the light beam passes through the measurement area, the specific gas molecule will absorb light of a specific wavelength, so that the intensity of the light beam in this band is reduced, and the ratio of the light intensity before and after absorption The concentration of the gas is directly related, and the absorption band and intensity of the gas sample

d ¼ nλ<sup>1</sup> and d ¼ ð Þ n þ 1 λ<sup>2</sup> (1) λ<sup>1</sup> ¼ d*=*n and λ<sup>2</sup> ¼ d*=*ð Þ n þ 1 (2) ν<sup>1</sup> ¼ 1*=*λ<sup>1</sup> and ν<sup>2</sup> ¼ 1*=*λ<sup>2</sup> (3) ν<sup>1</sup> ¼ n*=*d and ν<sup>2</sup> ¼ ð Þ n þ 1 *=*d (4)

ν<sup>2</sup> � ν<sup>1</sup> ¼ 1*=*d (5)

membership function in the following Eqs. (1)�(5) [20, 21]:

*Linked Open Data - Applications,Trends and Future Developments*

*The instrument configuration of the pumped FTIR.*

**Figure 4.**

**84**

**Figure 6.** *The research cloud computing system and database architecture.*

The process of data decomposition is defined as follows: Let X is defined as the number of groups and Y as several data, as shown below Eqs. (7)�(8).

$$\mathbf{X} = (\mathbf{Y}/\mathbf{10}, \mathbf{000})\tag{7}$$

Find k, for which ck = max ci.

*DOI: http://dx.doi.org/10.5772/intechopen.92849*

hk is the optimal choice of value for the selected object. If k has more than

*Study on IoT and Big Data Analysis of 12" 7 nm Advanced Furnace Process Exhaust Gas Leakage*

Forming up the n � n discernibility matrix. The elements of S table is defined as d(x, y) = a ∈ A | f (x, a) ̸= f (y, a), d(x, y) is an attribute. set distinguishing x and y. For each attribute a ∈ A, if d(x, y) = a1, a2, ...,

Formulate the Boolean function a1 ∨ a2... ∨ ak or discernibility function which represented by Σ d(x, y) as indicated: F (A) = Π (x,y)∈U � U) Σ d

If d(x, y) = ∅, constant 1 will be assigned to the Boolean function. Execute the attribute reduction process based on the simplified Boolean

In the reactor, chemical reaction is used to form the reactant (usually a gas) into a solid product, and a thin film is deposited on the surface of the wafer. This process is called CVD (Chemical Vapor Deposition). This process has (1) good step coverage, (2) energy with high aspect ratio gap filling, (3) good thickness uniformity, (4) high pure and dense, (5) when ratio can be controlled, (6) low stress for high film quality, (7) good electrical properties, (8) base plate and excellent film adhesion characteristics. **Figure 7** shows the system architecture of CVD

The precipitation of products during the CVD process can be divided into the following steps: (1) the source gas diffuses to the substrate surface, (2) the substrate adsorbs the source gas, (3) the substances adsorbed on the substrate react chemically on its surface, (4) The precipitated material diffuses on the surface of the substrate, (5) The reaction product is separated from the gas-phase reactant, (6) The precipitated non-volatile material is deposited on the substrate surface by diffusion and the like. The composition, structure and performance of the products obtained in this chemical reaction can be controlled by changing the parameters of the reaction. The reaction parameters mainly include the type of gas, the gas reaction concentration, the delivery method of the reactant, the gas flow rate, the total gas pressure and the Area pressure, heating method, substrate material, substrate surface state, substrate reaction temperature, temperature distribution and

When entering the 7-nanometer process, the channel material of the semiconductor PN junction must also be changed. Since the electron mobility of silicon is 1500 cm<sup>2</sup> / Vs, and germanium can reach 3900 cm<sup>2</sup> / Vs, and the implementation

*j*

*hi*,*<sup>j</sup>* and hi,j is the

one value, any one of the benefits could be chosen. ci is the choice of value of an object hi where *ci* <sup>¼</sup> <sup>P</sup>

entries in the table of the reduct soft set. Algorithm (3): Rough set parameter reduction algorithm.

f is an information function that maps an. object in U to exactly one value in V. Output: Simplified reduct sets. Input the information Table S.

New optimized reduct sets are generated.

Input: An information system S = (U, A, V, f). U is a finite nonempty set object. A is a finite nonempty set of attributes.

V is a nonempty set of values.

Discretization of data.

ak ̸= ∅.

(x, y).

function.

in FAB.

gradient, etc.

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**2.3 Process design and discussion**

If X contains remainder, then

$$\mathbf{X} = \mathbf{X} + \mathbf{1} \tag{8}$$

Where the number of groups will be added to 1.

Algorithm (1): The most optimized attribute set searching algorithm.


Select the highest number of attribute values, HR if HR does not have the same number with attribute value AND HR has more than one value then.

Select the first reduction set, FR of attribute values.

else

Proceed to the next process

else

Proceed to the next process

Algorithm (2): Soft set parameter reduction algorithm.

In tabular representation, let (F, P) represent the soft set. If Q is the reduction of P, the soft set reduction set is defined as (F, Q) of the soft set (F, P) where P ⊂ E. Input: A soft set (F, E), set P.

Output: Optimal decision.

Input the set P of choice parameters.

Find all reducts of (F, P).

Select one reduct set (F, Q) of (F, P).

Find weighted table of soft set (F, Q) according to the decided weights.

Find k, for which ck = max ci. hk is the optimal choice of value for the selected object. If k has more than one value, any one of the benefits could be chosen. ci is the choice of value of an object hi where *ci* <sup>¼</sup> <sup>P</sup> *j hi*,*<sup>j</sup>* and hi,j is the entries in the table of the reduct soft set. Algorithm (3): Rough set parameter reduction algorithm. Input: An information system S = (U, A, V, f). U is a finite nonempty set object. A is a finite nonempty set of attributes. V is a nonempty set of values. f is an information function that maps an. object in U to exactly one value in V. Output: Simplified reduct sets. Input the information Table S. Discretization of data. Forming up the n � n discernibility matrix. The elements of S table is defined as d(x, y) = a ∈ A | f (x, a) ̸= f (y, a), d(x, y) is an attribute. set distinguishing x and y. For each attribute a ∈ A, if d(x, y) = a1, a2, ..., ak ̸= ∅. Formulate the Boolean function a1 ∨ a2... ∨ ak or discernibility function which represented by Σ d(x, y) as indicated: F (A) = Π (x,y)∈U � U) Σ d (x, y). If d(x, y) = ∅, constant 1 will be assigned to the Boolean function. Execute the attribute reduction process based on the simplified Boolean function.

New optimized reduct sets are generated.
