Introduction to Modern Control Theory: Introduction to state‐space versus transform methods in linear systems; internal versus input/output formulation; discrete‐time and continuous‐ time systems; Solution to LTI and LTV systems for homogeneous and non-homogeneous cases. Computation of matrix exponentials using Laplace transforms and Jordan Normal form. Applications of Eigen values and Eigen vectors.

Introduction to Nonlinear systems: Non-linear elements in control systems, overview of analysis methods. Phase plane analysis: Concepts of phase plane analysis, Phase plane analysis of linear and nonlinear systems, Existence of limit cycles. Fundamentals of Liapunov theory: Nonlinear systems and equilibrium points, Concepts of stability, Linearization and local stability, Lyapunov’s direct method, Invariant set theorems, Lyapunov analysis of LTI systems, Krasovskii’s method, Variable gradient method, Physically motivated Lyapunov functions.

Classical scaling in CMOS, Moore’s Law, Clean room concept, Material properties,crystal struc- ture, lattice, Growth of single crystal Si, Cleaning and etching, Thermaloxidation, Dopant diffu- sion in silicon, Deposition & Growth (PVD, CVD, ALD,epitaxy, MBE, ALCVD etc.),Ion-im- plantation, Lithography (Photolithography, EUVlithography, X-ray lithography, e-beam lithog- raphy etc.), Etch and Cleaning, CMOSProcess integration, Back end of line processes (Copper damascene process, Metalinterconnects; Multi-level metallization schemes), Advanced technol- ogies(SOIMOSFETs, Strained Si, Silic

Basics: Control system representations, System stabilities, Coprime factorization and stabilizing controllers, Signals and system norms Modelling of uncertain systems: Unstructured Uncertain- ties, Parametric uncertainty, Linear fractional transformation, Structured uncertainties.

Robust design specifications: Small gain theorem and robust stabilization, Performance con- siderations, Structured singular values.

Design: Mixed sensitivity optimization, 2-Degree of freedom design, Sub-optimal solutions, Formulae for discrete time cases.

Introduction and historical background, Microsensors : Sensors and characteristics, Integrated Smart sensors, Sensor Principles/classification-Physical sensors (Thermal sensors, Electrical Sensors, tactile sensors, accelerometers, gyroscopes , Proximity sensors, Angular displacement sensors, Rotational measurement sensors, pressure sensors, Flow sensors, MEMS microphones etc.), Chemical and Biological sensors (chemical sensors, molecule-based biosensors, cell-based biosensors), transduction methods(Optical, Electrostatic, Electromagnetic, Capacitive, Piezoelectric, piezoresistive etc.), Mic

Soft switching Converters: Switching loss, basic principles of hard and soft switching.

Evolution of resonant converter topologies. Resonant Load Converters: analysis of series, parallel, LCC and LLC topologies. Resonant Switch Converters (quasi resonant). Resonant Transition Converters- phase modulated topologies, Soft Switched low power high frequency converters
with Auxiliary Switch. Emerging trends in low power high frequency soft switched converters.

Module 1: Role of Interface Electronics, Analog Electronic Blocks, OPAMP – internal structure, Open-loop gain, Input R, Output R, DC noise sources and their drifts, CMRR, PSRR, Bandwidth and stability, Slew rate, Noise – general introduction,OPAMP Circuits and Analysis - Difference and Instrumentation Amplifiers (3-opamp and 2-opamp), Effect of cable capacitance and wire-re- sistance on CMRR, IA with guards, Biomedical application, Current-mode IA (Howland), Current- input IA, filters, Filters with underdamped response, state-variable filters, All-pass filters, Current Sour

Machine learning basics: capacity, overfitting and underfitting, hyperparameters and validation sets, bias & variance; PAC model; Rademacher complexity; growth function; VC-dimension; fundamental concepts of artificial neural networks; single layer perceptron classifier; multi- layer feed forward networks; single layer feed-back networks; associative memories; introduc- tory concepts of reinforcement learning, Markhov decision process.

Optimization: need for unconstrained methods in solving constrained problems, necessary con- ditions of unconstrained optimization, structure methods, quadratic models, methods of line search, steepest descent method; quasi-Newton methods: DFP, BFGS, conjugate-direction meth- ods: methods for sums of squares and nonlinear equations; linear programming: simplex meth- ods, duality in linear programming, transportation problem; nonlinear programming: Lagrange multiplier, KKT conditions, convex programing

Linear Algebra: n- dimensional Euclidean spaces, linear transformation, Matrices, Eigen values and Eigen vectors, Generalised inverses, SVD.

Numerical Methods: Numerical Solution of nonlinear equations, Direct and iterative methods to solve system of linear equations, Numerical integration – Trapezoidal and Simpson’s rule, Interpolation, Splines and curve fitting, Numerical solution of ODE – Euler’s method and 4th order Runge- Kutta Method.