IE121Presentation

Group MemberPanita Suthiwongsing 5310750129

Kittiwat Watanasungsuit 5310750731

Penpitcha Rungruangpan 5310755698

SEMICONDUCTOR

- Overview- Structure- Properties on both advantages and limitations- Applications

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The process of IC manufacturing often requires hundreds of sequential steps, each one of which could lead to yield loss. Consequently, maintaining product qualityin an IC manufacturing facility often requires the strict control of hundreds or even thousands of process variables. The issues of high yield, high quality and low cycle time are being addressed in part by the ongoing development of several critical capabilities:

- Process Monitoring- Process/Equipment Modeling - Process Optimization - Process Control - Equipment Malfunction Diagnosis - Parametric Yield Modeling

SEMICONDUCTOR MANUFACTURING

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Process MonitoringThe most important step in semiconductor process modeling is the collection of data. It is essential to gather a sufficient sample of representative data; or else it is impossible to train a neural network or any other type of model. As an example of a methodology for collecting real-time data for semiconductor process modeling or process control, consider the project In-Situ Monitoring of Reactive Ion Etching. This project describes an effort to design a real-time data acquisition system for RIE as well as the development and implementation of a prototype microsensor for monitoring of film characteristics during etching. This sensor will facilitate in-situ characterization of etch rate on the wafer surface, a significant contribution to the state of the art. The microsensor concept has evolved further in the project A Smart Sensor for Monitoring Reactive Ion Etching. Another project in the area of process monitoring is A Piezoelectric Silicon Acoustic Sensor, in which the goal is to investigate the use of acoustic signals for monitoring of plasma processes.

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Process ModelingSeveral researchers have of late reported noteworthy successes in using neural nets to model the behavior of several key fabrication processes. Here the ability of neural nets to discover input/output relationships from limited data is very useful. At the Georgia Tech Microelectronics Research Center (MiRC), back-propagation (BP) neural networks have been used to model ion-assisted plasma etching, as well as a variety of other processes widely used in semiconductor manufacturing. The models are required by semiconductor manufacturers in order to predict process behavior under an exhaustive set of operating conditions, and with a very high degree of precision. However, plasma processing involves highly complex and dynamic interactions between reactive particles in an electric field.

Due to this inherent complexity, approaches to plasma etch modeling which preceded the advent of neural networks had met with limited success. Other processes besides plasma etching have benefited from the neural network approach. To name one, chemical vapor deposition processes are also nonlinear and also have been modeled by these means to good effect. Researchers at the MiRC have developed neural process models for plasma-enhanced chemical vapor deposition of silicon dioxide films. Process models have also been developed for metal-organic chemical vapor deposition(MOCVD) and molecular beam epitaxy (MBE), two popular techniques for growing thin, high purity epitaxial films.

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Process OptimizationA natural extension of the neural modeling of processes is using the models thus obtained to optimize processes or generate recipes for them. This optimization is designed to produce designated target output responses based on the functional relationship between controllable input parameters and process responses supplied by the neural models. Such a process optimization activities has been undertaken in the projects Plasma Enhanced Chemical Vapor Deposition and Recipe Synthesis for PECVD and Process Modeling and Recipe Synthesis of Integrated Capacitors.

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Process ControlSince they can be used to build predictive models from multivariate sensor data generated by process monitors, neural nets have also been applied to the control of semiconductor manufacturing processes. The Intelligent Semiconductor Manufacturing Group is participating in process control efforts through the projects Real Time Control and Endpoint Detection of Reactive Ion Etching, Real-Time Control of Plasma Etching Using Integrated Sensors and Adaptive Neural Networks, Modeling and Control of Variable Frequency Microwave Processing, and Modeling and Control of Molecular Beam Epitaxy using the RHEED Diffraction Pattern.

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Equipment and Process DiagnosisThe use of Neural networks for the diagnosis of semiconductor manufacturing processes and equipment is relatively new. Toward this end, the Intelligent Semiconductor Manufacturing group has adopted the strategy of using neural networks in tandem with expert systems in the projects Automated Malfunction Diagnosis Of A Reactive Ion Etcher, Real-Time Diagnosis of Reactive Ion Etch Using Optical Emission Spectroscopy, and Modeling and Diagnosis of Excimer Laser Ablation

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Parametric Yield ModelingHistorically, IC yield enhancement has focused on the reduction of manufacturing anomalies such as particulate matter, mechanical damage, and crystalline defects. Under this paradigm, circuit failure is primarily characterized internal opens or shorts, so-called "catastrophic" defects which result in a loss of functionality. However, degradation in IC performance can result from sources other than catastrophic defects alone. In fact, even in a defect-free fabrication environment, random variations in the manufacturing process itself will lead to products with varying levels of performance. These manufacturing variations result from the fluctuation of various physical parameters (i.e. - doping concentrations, oxide thicknesses, line widths, etc.), which in turn manifest themselves first as variations in IC device parameters (such as threshold voltages), and finally as variations in circuit performance measures (such as bandwidth).

Accurate IC performance prediction requires precise characterization of these variations. Tools are therefore necessary which are capable of modeling circuit parametric performance based on device and manufacturing variation, rather than simply relying on nominal parameter values. Neural networks and various statistical methods are used as parametric yield modeling tools in the projects Statistical Prediction of Integrated Circuit Performance, Reliability Modeling and Parametric Yield Prediction of GaAs MQW Avalanche Photodiodes, and Design and Optimization of Microwave Circuits and Systems Using Artificial Intelligence.

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Structure of SemiconductorSemiconductors are made up of individual atoms bonded together in a regular, periodic structure to form an arrangement whereby each atom is surrounded by 8 electrons. An individual atom consists of a nucleus made up of a core of protons (positively charged particles) and neutrons (particles having no charge) surrounded by electrons. The number of electrons and protons is equal, such that the atom is overall electrically neutral. The electrons occupy certain energy levels, based on the number of electrons in the atom, which is different for each element in the periodic table. The structure of a semiconductor is shown in the figure below. Schematic representation

of covalent bondsin a silicon crystal lattice.

The atoms in a semiconductor are materials from either group IV of the periodic table, or from a combination of group III and group V (called III-V semiconductors), or of combinations from group II and group VI (called II-VI semiconductors). Silicon is the most commonly used semiconductor material as it forms the basis for integrated circuit (IC) chips and is the most mature technology and most solar cells are also silicon based. A full periodic table is given in the page Periodic Table. Several of the material properties of silicon are given in the page Silicon Material Parameters. The bond structure of a semiconductor determines the material properties of a semiconductor. One key effect is limit the energy levels which the electrons can occupy and how they move about the crystal lattice. The electrons surrounding each atom in a semiconductor are part of a covalent bond. A covalent bond consists of two atoms "sharing" a single electron, such that each atom is surrounded by 8 electrons.

The electrons in the covalent bond are held in place by this bond and hence they are localised to region surrounding the atom. Since they cannot move or change their energy, electrons in a bond are not considered "free" and cannot participate in current flow, absorption or other physical processes of interest in solar cells. However, only at absolute zero are all electrons in a bonded arrangement. At elevated temperatures, the electron can gain enough energy to escape from its bond, and if this happens, the electron is free to move about the crystal lattice and participate in conduction. At room temperature, a semiconductor has enough free electrons to allow it to conduct current, while at, or close to absolute temperatures, a semiconductor behaves like an insulator. The presence of the bond introduces two distinct energy states for the electrons. The lowest energy position for the electron is to be in its bound state. However, if the electron has enough thermal energy to break free of its bond, then it becomes free.

The electron cannot attain energy values intermediate to these two levels; it is either at a low energy position in the bond, or it has gained enough energy to break free and therefore has a certain minimum energy. This minimum energy is called the "band gap" of a semiconductor. The number and energy of the free electrons is basic to the operation of electronic devices. The space left behind by the electrons allows a covalent bond to move from one electron to another, thus appearing to be a positive charge moving through the crystal lattice. This empty space is commonly called a "hole", and is similar to an electron, but with a positive charge.

Animation showing formation of "free" electrons and holes when an electron can escape its bond.

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Applications of SemiconductorAll transistor types can be used as the building blocks of logic gates, which are fundamental in the design of digital circuits. In digital circuits like microprocessors, transistors act as on-off switches; in the MOSFET, for instance, the voltage applied to the gate determines whether the switch is on or off. Transistors used for analog circuits do not act as on-off switches; rather, they respond to a continuous range of inputs with a continuous range of outputs. Common analog circuits include amplifiers and oscillators.Circuits that interface or translate between digital circuits and analog circuits are known as mixed-signal circuits.Power semiconductor devices are discrete devices or integrated circuits intended for high current or high voltage applications. Power integrated circuits combine IC technology with power semiconductor technology, these are sometimes referred to as "smart" power devices. Several companies specialize in manufacturing power semiconductors

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Semiconducture PropertiesA pure semiconductor such as silicon has uniform properties, with the same band gap and lattice properties. A simple binary semiconductor such as GaAs or InP also has uniform properties.You can get other properties by adding one or two other elements from the same groups of the periodic table to a compound semiconductor. This is equivalent to blending different materials to get a new compound with intermediate characteristics. For example, a mixture of 20% AlAs and 80% GaAs has band gap energy and lattice spacing partway between pure AlAs and pure GaAs. The formula for such a compound can be written Ga0.8Al0.2As or simply GaAlAs. (The order of the two elements that replace each other-aluminum and gallium-is arbitrary; some people write AlGaAs.)Things get more complex if the compound semiconductor contains four elements. Depending on the relative concentrations of the elements, such "quaternary" semiconductors can have properties somewhere in a broad range.

For example, as shown in Figure 9-20, InGaAsP (more precisely InxGa1 xAs1 yPy) can have lattice spacing and bandgap within an area defined by four possible binary compounds: InAs, InP, GaAs, and GaP. The material's properties can vary within the entire space because the In Ga and As P ratios can be adjusted independently. (The odd shape of the area in the figure arises because pure GaP is an indirect band gap material, unsuitable for lasers and somewhat different from the other binary compounds. The dashed line indicates the indirect band gap region.) Plots like Figure 9-20 are very helpful in understanding semiconductor properties; they are commonly drawn with lattice spacing on the bottom, but some have lattice spacing on the sides and band gap on the bottom.The band gaps of different layers in a semiconductor device do not have to match, but the lattice spacing must be close to avoid flaws that degrade device performance. This means that all compositions in a bulk InGaAsP structure must fall roughly on a vertical line in Figure 9-20. The use of thin strained layers relaxes this constraint somewhat, as mentioned earlier.

Semiconductor laser structures are grown on substrates of simple-to-produce binary materials such as InP or GaAs, which account for most of the device volume. The choice of substrate restricts composition of other layers to materials with similar lattice spacing.

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Example of semiconductor

Diode

IC & Transistor

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Reference :

http://users.ece.gatech.edu/~gsm/overview.html

http://pvcdrom.pveducation.org/SEMICON/SEMICON.HTM

http://www.classle.net/bookpage/semiconductor-and-pn-junctions

http://www.globalspec.com/reference/13700/160210/chapter-9-11-1-semiconductor-properties-and-composition

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THANKS YOU