Final Words

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In this thesis, a basic introduction on the construction of a MOSFET is given. The general output characteristics of the MOSFET are discussed, and the derivation of output characteristics based on square law and bulk charge theories are demonstrated. As the working temperature of a MOSFET is critical, the dependency of threshold voltage, saturation current and electron mobility on the temperature of the device is discussed. It was found that threshold voltage and electron mobility decrease as temperature decreases, whereas the saturation current increase as temperature increases for low values of gate-to-source voltage but decrease similar to electron mobility for higher value of gate-to-source voltage. The concept of velocity saturation is introduced, and it was found that the MOSFET is a velocity saturated device.

An explanation on the working principle of MOSFET as a energy barrier controlled device is given, and with it some critical limitations of MOSFET are discussed such as minimum energy required for a switching event, the minimum gate length and the minimum time of a switching event. They are calculated to be 0.017eV, 1.5nm and 37.7fs respectively.

The dependencies of electrical properties of a MOSFET such as threshold voltage and transconductance on the physical properties of a MOSFET such as gate oxide thickness, gate length, source/drain and channel doping concentrations are simulated and analysed. The dependence of output resistance on the gate length is analysed as well. It was found that as gate oxide thickness increase, the threshold voltage increased and the transconductance decreased linearly. For increase in gate length, the threshold voltage increased linearly and saturated after a certain length. Whereas the transconductance decreased linearly and saturated after a certain value of gate length. When the heavy drain doping concentration is increased, the threshold voltage remained the same whereas the transconductance increased slightly. Increasing the channel doping concentration resulted in a slight increase in threshold voltage, whereas the transconductance remained almost same with negligible variations.

All the dependencies discussed above were also simulated for a MOSFET with a metal gate. It was found that the metal gate performed slightly better in certain aspects. As feature sizes of transistors are reduced greatly, metal gates in conjunction with high-k di-electrics are a preferred choice as they provide better performance than polysilicon gate devices.

In a bid to extract better device performance, alternate materials, innovative packaging and transistor stacking topologies are being used which have resulted in complex equations for analysis of an individual transistor and the over-all integrated chip. The use of Machine Learning to eliminate use of equations in analysis was investigated. Artificial Neural Networks seem to be a promising tool in this regard. Datasets were created with a range of information. A number of classification models were trained to predict the operating region of a MOSFET for a given value of threshold voltage, gate-to-source and drain-to-source voltage. It was found that a Logistic Regression classifier was more accurate in correctly classifying the operating region. Regression models were used to predict a transistor parameter such as threshold voltage, transconductance or saturation current based on one or more properties like gate oxide thickness, gate length, gate voltage, threshold voltage and saturation current. It was seen that variants of Gaussian Process regression models predicted better with a lower RMSE in case of threshold voltage and transconductance, while a Quadratic SVM Regression model predicted saturation current with a lower RMSE.

To continue on the work done in this thesis, a device with a substrate other than silicon, along with a high-k di-electric material and a metal gate maybe constructed and analysed for its performance. Values for the said limitations of a MOSFET may also be calculated for this new device. Another avenue for improvement maybe to study the effects of varying the width of a MOSFET on its performance. The results maybe used to compare performance improvements, if any, to existing technologies, and conclude on the feasibility of the new device.

The properties of the underlying mathematical functions of various classification and regression models used in this thesis maybe tweaked to improve the accuracy of the models. Alternatively, new models along with new data such as doping concentrations or device temperature which are not used in this thesis may also be analysed for their predictive performance.

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