# Review of Literature
## Airborne Molecular Contamination

### Original
- [ ] AMCs: is a generic term encompassing gaseous- or vapor-state pollutants from any emission that are detrimental to any of the wafer fabrication process. Control of AMC has become a crucial element of cleanroom management as the production phase of semiconductor devices marches deep into sub-100 nm range, which is the same order of the size of the molecule.
- [ ] Airborne Molecular Contamination (AMC)
These pollutants that are highly diluted in air and can move through it (commonly called
chemical contamination) have been considered a main source of wafer defects and poor
semiconductor devices efficiency. Meanwhile the semiconductor industry has increased the
wafer diameter size and reducing integrated circuits dimensions at the same time (more
Integrated Circuits per Si-wafer results more profitable and increases mass production,)
AMCs become more dangerous for the products causing a wide range of problems that affect
in several ways the reliability of the components. For that reason, cleanliness requirements
have been more rigorous due to the quick miniaturization of ICs during past years. The air
quality and physical factors (Temperature, Pressure, Relative Humidity and Vibrations)
inside the cleanroom must be highly controlled and managed in order to reduce potential
hazardous environments

### Paraphrase

- [x] AMC (Airborne Molecular Contamination) is a terminology for gaseous or vapor state pollutants generated during the process of the wafer fabrication process. These contaminants are solubilized in air and have been identified as the major source of wafer defects. AMCs are becoming harmful to the products, have caused a variety of issues that impact the quality of the parts in a variety of ways. As a result of the rapid manufacturing of ICs in recent years, cleanliness requirements have become more stringent. The physical and air quality factors (Temperature, Pressure, Relative Humidity, and Vibrations). To reduce potential risks in hazardous environments, the cleanroom must be tightly controlled and managed.

## Computational Fluid Dynamics (CFD)

### Paraphrase
- [x] Many researchers around the world have studied the behavior of jets in mechanically ventilated rooms, all experimentally and theoretically, since the 1940s. When simulating working conditions, computational fluid dynamics (CFD) has the advantages of being economical, rapid, and giving comprehensive data. Nielsen in Denmark was the first to apply CFD techniques to the simulation of air flow in ventilated rooms in 1974.

- [x] Computational fluid dynamics, or CFD, is the computer-based simulation of systems involving fluid flow, heat transfer, and related processes such as chemical reactions. The technique is highly effective and also has a wide range of academic or industrial applications. When simulating working conditions, computational fluid dynamics (CFD) has the advantages of being economical, rapid, and giving comprehensive data.

The main goal of CFD improvements is to have a capability comparable to other CAE (computer-aided engineering) software. CFD codes are organized around numerical algorithms capable of resolving fluid flow problems. To facilitate access to their computational capability, all commercial CFD software include efficient user interfaces for parameter input and analysis. As a result, all codes contain three primary components: (i) pre-processor, (ii) solver, and (iii) post-processor.

**Pre-processor**
Is the process of inputting a flow problem into a CFD software and then converting the input into a logical format to be used by the solver.

During the pre-processing stage, users do the following tasks:
- [ ] Building the computational domain by defining the geometry of the area of interest
- [ ] Grid generation
- [ ] Identification of physical and chemical properties to be simulated
- [ ] Fluid property definition.

The number of cells in the grid determines the accuracy of a CFD solution. In general, the greater the number of cells, the greater the accuracy of the solution. In industry, the definition of the domain geometry and grid generation consume more than half of the time spent on a CFD project.

**Solver**
At this point, we must specify the conditions of the situations we want to solve, such as fluid material properties, flow physics models, and boundary conditions.
CFD codes contain discretisation techniques that compensate for convection, diffusion, the source term, and the rate of change with respect to time.


The numerical algorithm in CFD can be summarized as follows:
• Integration of the fluid flow governing equations over all (finite) control volumes in the domain• Discretisation-conversion of the resulting integral equations to a system of algebraic equations; • Iterative solution of the algebraic equations.

**Post-Processor**

It is necessary to evaluate the data using various methods such as contour plots, vector plots, streamlines, and data curves in order to create an acceptable visual display and report the results after they have been obtained.

### Reynolds-Averaged Approach vs. LES

#### Original
Time-dependent solutions of the Navier-Stokes equations for high Reynolds-number turbulent flows in complex geometries which set out to resolve all the way down to the smallest scales of the motions are unlikely to be attainable for some time to come. Two alternative methods can be employed to render the Navier-Stokes equations tractable so that the small-scale turbulent fluctuations do not have to be directly simulated: Reynolds-averaging (or ensemble-averaging) and filtering. Both methods introduce additional terms in the governing equations that need to be modeled in order to achieve a "closure'' for the unknowns [1].

The Reynolds-averaged Navier-Stokes (RANS) equations govern the transport of the averaged flow quantities, with the whole range of the scales of turbulence being modeled. The RANS-based modeling approach therefore greatly reduces the required computational effort and resources, and is widely adopted for practical engineering applications. An entire hierarchy of closure models are available in ANSYS FLUENT including Spalart-Allmaras, $k$- $\epsilon$ and its variants, $k$- $\omega$ and its variants, and the RSM. The RANS equations are often used to compute time-dependent flows, whose unsteadiness may be externally imposed (e.g., time-dependent boundary conditions or sources) or self-sustained (e.g., vortex-shedding, flow instabilities).

LES provides an alternative approach in which large eddies are explicitly computed (resolved) in a time-dependent simulation using the "filtered'' Navier-Stokes equations. The rationale behind LES is that by modeling less of turbulence (and resolving more), the error introduced by turbulence modeling can be reduced. It is also believed to be easier to find a "universal'' model for the small scales, since they tend to be more isotropic and less affected by the macroscopic features like boundary conditions, than the large eddies. Filtering is essentially a mathematical manipulation of the exact Navier-Stokes equations to remove the eddies that are smaller than the size of the filter, which is usually taken as the mesh size when spatial filtering is employed as in ANSYS FLUENT. Like Reynolds-averaging, the filtering process creates additional unknown terms that must be modeled to achieve closure. Statistics of the time-varying flow-fields such as time-averages and r.m.s. values of the solution variables, which are generally of most engineering interest, can be collected during the time-dependent simulation.

LES for high Reynolds number industrial flows requires a significant amount of computational resources. This is mainly because of the need to accurately resolve the energy-containing turbulent eddies in both space and time domains, which becomes most acute in near-wall regions where the scales to be resolved become much smaller. Wall functions in combination with a coarse near wall mesh can be employed, often with some success, to reduce the cost of LES for wall-bounded flows. However, one needs to carefully consider the ramification of using wall functions for the flow in question. For the same reason (to accurately resolve the eddies), LES also requires highly accurate spatial and temporal discretizations.

### Paraphrase

The Reynolds-averaged Navier-Stokes (RANS) equations govern the transport of averaged flow properties, encompassing the entire range of turbulence sizes. As a result of the significant reduction in computational work and resource requirements, the RANS-based modeling approach is widely used in practical engineering applications. *ANSYS FLUENT provides a whole hierarchy of closure models, namely Spalart-Allmaras, $k$- $\epsilon$ and its variants, $k$- $\omega$ and its variants, and the RSM*.

## Front Opening Unified Pod (FOUP)

### Original
- [ ] FOUP purging is one of the current ways practiced by semiconductor industries in wafer ambient contaminants removal through the displacement and dilution effects combination [12]. What happens in displacement is when the purged gas pushes the contaminated air outside of the FOUP while bathing the wafers with inert gas. However, with dilution, purged gas mixes with contaminated air present inside the FOUP making the concentration of contaminants lower compared to the initial. Reduction or elimination of contaminants deposition on wafer surfaces is made effective because of this two processes simultaneously working during purging

- [ ] Moisture has long been recognized as a disruptive element compromising the reliability of semiconductor devices during the modern manufacturing process. The most common technology to effectively prevent wafers from exposure to ambient conditions is the use of FOUP.

### Paraphrase

Moisture is known as a disruptive factor in the modern manufacturing process, affecting the reliability of semiconductor devices. FOUP is the most used method for effectively protecting wafers from environmental influences.
FOUP purging is known as the method used in the semiconductor industry to remove wafer ambient impurities through a mix of displacement and dilution effects. Displacement occurs when purged gas forces contaminated air out of the FOUP while soaking the wafers in inert carrier gas such as N2. Furthermore, dilution occurs when purged gas interacts with polluted air present inside the FOUP, resulting in a lower amount of contaminants than earlier.