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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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PSZ JB | 30000010222023 | TS171 O67 2010 | Open Access Book | Searching... |

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### Summary

### Summary

Explore the frontier of device engineering by applying optimization to nanoscience and device design. This cutting-edge work shows how robust, manufacturable designs that meet previously unobtainable system specifications can be created using a combination of modern computer power, adaptive algorithms, and realistic device-physics models. Applying this method to nanoscience is a path to creating new devices with new functionality, and it could be the key design element in making nanoscience a practical technology. Basic introductory examples along with MATLAB code are included, through to more formal and sophisticated approaches, and specific applications and designs are examined. Essential reading for researchers and engineers in electronic devices, nanoscience, materials science, applied mathematics, and applied physics.

### Author Notes

A. F. J. Levi is Professor of Electrical Engineering and of Physics and Astronomy at the University of Southern California. He joined USC after working for 10 years at ATT Bell Laboratories, New Jersey. Professor Levi is the author of the book Applied Quantum Mechanics, Second Edition (Cambridge University Press, 2006).

Stephan Haas is professor of Theoretical Condensed Matter Physics at the University of Southern California.

### Table of Contents

Preface | p. ix |

Acknowledgements | p. xi |

1 Frontiers in device engineering | p. 1 |

1.1 Introduction | p. 1 |

1.2 Example: Optimal design of atomic clusters | p. 3 |

1.3 Design in the age of quantum technology | p. 6 |

1.4 Exploring nonintuitive design space | p. 14 |

1.5 Mathematical formulation of optimal device design | p. 15 |

1.6 Local optimization using the adjoint method | p. 18 |

1.7 Global optimization | p. 21 |

1.8 Summary | p. 28 |

1.9 References | p. 29 |

2 Atoms-up design | p. 32 |

2.1 Manmade nanostructures | p. 32 |

2.2 Long-range tight-binding model | p. 35 |

2.3 Target functions and convergence criterion | p. 36 |

2.4 Atoms-up design of tight-binding clusters in continuous configuration space | p. 38 |

2.5 Optimal design in discrete configuration space | p. 42 |

2.6 Optimization and search algorithms | p. 45 |

2.7 Summary | p. 48 |

2.8 References | p. 49 |

3 Electron devices and electron transport | p. 51 |

3.1 Introduction | p. 51 |

3.2 Elastic electron transport and tunnel current | p. 57 |

3.3 Local optimal device design using elastic electron transport and tunnel current | p. 61 |

3.4 Inelastic electron transport | p. 71 |

3.5 Summary | p. 85 |

3.6 References | p. 86 |

4 Aperiodic dielectric design | p. 88 |

4.1 Introduction | p. 88 |

4.2 Calculation of the scattered field | p. 89 |

4.3 Optimization | p. 91 |

4.4 Results | p. 93 |

4.5 Efficient local optimization using the adjoint method | p. 103 |

4.6 Finite difference frequency domain electromagnetic solver | p. 104 |

4.7 Cost functional | p. 107 |

4.8 Gradient-based optimization using the adjoint method | p. 108 |

4.9 Results and comparison with experiment | p. 109 |

4.10 References | p. 120 |

5 Design at the classical-quantum boundary | p. 123 |

5.1 Introduction | p. 123 |

5.2 Non-local linear response theory | p. 124 |

5.3 Dielectric response of a diatomic molecule | p. 126 |

5.4 Dielectric response of small clusters | p. 129 |

5.5 Dielectric response of a metallic rod | p. 135 |

5.6 Response of inhomogeneous structures | p. 137 |

5.7 Optimization | p. 141 |

5.8 Summary and outlook | p. 147 |

5.9 References | p. 147 |

6 Robust optimization in high dimensions | p. 149 |

6.1 Introduction | p. 149 |

6.2 Unconstrained robust optimization | p. 152 |

6.3 Constrained robust optimization | p. 170 |

6.4 References | p. 186 |

7 Mathematical framework for optimal design | p. 189 |

7.1 Introduction | p. 189 |

7.2 Constrained local optimal design | p. 194 |

7.3 Local optimal design of an electronic device | p. 204 |

7.4 Techniques for global optimization | p. 228 |

7.5 Database of search iterations | p. 237 |

7.6 Summary | p. 244 |

7.7 References | p. 244 |

8 Future directions | p. 246 |

8.1 Introduction | p. 246 |

8.2 Example: System complexity in a small laser | p. 247 |

8.3 Sensitivity to atomic configuration | p. 251 |

8.4 Realtime optimal design of molecules | p. 257 |

8.5 The path to quantum engineering | p. 258 |

8.6 Summary | p. 259 |

8.7 References | p. 260 |

Appendix A Global optimization algorithms | p. 262 |

A.1 Introduction | p. 262 |

A.2 Tabu search | p. 262 |

A.3 Particle swarm algorithm | p. 263 |

A.4 Simulated annealing | p. 265 |

A.5 Two-phased algorithms | p. 268 |

A.6 Clustering algorithms | p. 269 |

A.7 Global optimization based on local techniques | p. 272 |

A.8 Global smoothing | p. 273 |

A.9 Stopping rules | p. 274 |

A.10 References | p. 275 |

About the authors | p. 277 |

Index | p. 281 |