Table of Contents Page #s Title Name Department 2-4 Development - - PowerPoint PPT Presentation

Table of Contents

Page #’s Title

Name Department

2-4 Development and Analysis of Mathematical Models Jared Whitehead Mathematics 5-12 Opportunities to Investigate novel Stable p-type ZnO David Allred Physics & Astronomy 13-22 Probabilistic Programming for Perceptually Driven Autonomous Agents David Wingate Computer Science 23-26 Computer Aided Proof, Stability of Traveling Waves Blake Barker Mathematics 27-35 Nanomagnetism Karine Chesnel Physics & Astronomy 36-40 Teaching Leadership in Teaching and Mentoring Denise Halverson Mathematics

Jared P. Whitehead

Mathematics Department

whitehead@mathematics.byu.edu Picture courtesy of Zhao Pan.

Development and Analysis of Mathematical Models

Topics of recent interest:

 Rigorous estimates (bounds) on physically interesting

statistical quantities in fluid dynamics.

 Quantification and validation of long-time dynamics of

climate, weather, and ocean modeling.

 Aspects of linear, and nonlinear stability theory

applied to finite and infinite dimensional stochastic dynamical systems.

 Mathematical modeling and the analysis of

mathematical models in the physical and social sciences.

Of far more interest...

We are starting a mathematical modeling center this fall

This means a bunch of mathematicians (with quantitatively skilled students to boot) want to hear about your problems.

If you have a modeling issue, or have something that you think some mathematicians may be able to help with, please get in touch with me and we'll put you on the calendar!

whitehead@mathematics.byu.edu

Opportunities to Investigate novel Stable p-type ZnO: Making progress with the Asymmetric dopant problem

David Allred Physics and Astronomy

allred@byu.edu (801) 422-3489

Areas of Interest: Thin films, Extreme Ultraviolet Optics, Nanostructures-MEMS, Mars Simulations, Alternate Energy Issues (Solar, Batteries, etc.)

5

Opportunities to Investigate novel Stable p-type ZnO: Making progress with the Asymmetric dopant problem

From Zeno

• For over 60 years p-type Wide-bandgap semiconductors have been a problem.
• ZnO has been described as a very desirable semiconductor materials.
• How to make stable forms of moderate to heavily p-type semiconductors,
• For example, for zinc-oxide this elusive goal has been driven by the

material’s desirable electronic, thermal, and mechanical properties.

• The traditional approach seems unlikely to succeed due to compound stability and

vapor pressure constraints on diffusional processes. Diffusion couples cannot be used to create long-term stable p-type zinc oxides.

• One technical team succeeded in producing stable p-type ZnO materials in

2007, produced over a range of dopant levels, from 1015 to 1021 atoms per cm3. By XPS arsenic concentrations at 0.25%, down a depth of 250 µm. “… this material I tested [Zeno Material’s p-type ZnO] is already good enough to produce good p-n junctions. The realization of a good, reliable, reproducible p-n junction in ZnO will cause an explosion of commercial interest.”

David Look, Ph.D., Director, Semiconductor Research Center – USAF

Mater erial als S Scien ence: T The A e Asymmetric D Dop

• pan

ant P Prob

• blem

em

Zeno Materials LLC

What is the problem?

Simply it is that atoms tend to go where they want to go not where the design would have them go.

Traditional fabrication methods of p-type ZnO have failed for decades to produce stable electronic materials. Zeno’s fabrication approach does not use dopant diffusion or ion implantation, it is fundamentally different to traditional methods. Dopants are essentially fixed within the unit cell. Zeno’s technology enables p-type ZnO fabrication that is inexpensive, controllable and reproducible. The process yields highly stable materials, with over 7 years of confirmed stability.

Zeno brings together seemingly incompatible elements The process does not rely on diffusion or ion implantation Producing highly stable dopants in ZnO

Innovation: New S Semiconductor M Material S Syst ystem

Zeno Materials LLC

Sputter onto a hot substrate

Interesting C Charact cteristics cs: Stability & & Mobility

1 Arsenic doped ZnO fabricated by Zeno Materials, sample was left in storage without sealant or special handling exposed to ambient air.. 2 Nitrogen doped ZnO, Fig.12, "Stability Studies on Nitrogen Doped p-ZnO (NZO) Thin Films Grown by Reactive Magnetron Sputtering". R.V. Muniswami Naidu et al, Journal of Display Technology, Vol.9, No.9, Sept. 2013 3 "ZnO Devices and Applications" Özgür et al (2010)

Zeno Materials 1 Best known competitor 2

P-type ZnO Stability comparison: Zeno vs. best performing conventionally fabricated

1020 1019 1018 1017 1016 1015

a-Silicon 3 Zeno

P-Type carrier mobility: a-Silicon vs. Zeno

cm2 • volt -1 • sec -1

1 2 3 4 5 6 7 Years

Hole mobility often limits overall mobility and semiconductor performance. The Company’s materials provide a hole mobility of about 220 cm2/volt•sec, nearly equal to that of the electron mobility.

P-type Charge Carrier Concentration / cm 3

Zeno Materials LLC

Equations: (AsO) Arsenic substitution on an oxygen site: ZnO1-x + Asx = 1 General equation* for “compounds”: [Zn4O4-n+ Asn] = 1 For n= 0 to 4 Consequences:

• Only specific stoichiometric compositions

are stable within the ternary diagram

• Other dopants besides Arsenic are viable

Strategy: prod

• duces

es s stab able p e p-type Z ZnO

0.2 0.4 0.6 0.8 1

Zinc - Arsenic

1 0.8 0.6 0.4 0.2

Oxygen - Arsenic

1 0.8 0.6 0.4 0.2

Zinc - Oxygen

Zinc

Oxygen

Arsenic

n=0: Zn4O4 n=1: [Zn4.5O3As] n=2: [Zn5O2As2] n=3: [Zn5.5OAs3] n=4: [Zn3As2] Dotted Line Indicates No Compositions Between Data Points

Zeno Materials LLC

* Assuming 2 unit cells with ZnO and Zn3As2 as compositional endpoints.

P-type ZnO made by Zeno Materials (~1019 p-type carriers, ρ= 0.008 Ω-cm)** on undoped Si.

* Allowing for a single frequency doubling

Material A Applications: p-ty type Z ZnO

Since the 1960s, p-type ZnO has been pursued for its predicted properties, which include:

• Direct wide bandgap semiconductor
• High thermal operating range (exciton binding energy ~60 meV)
• Radiation resistant semiconductor
• Can be doped as an emitter between ~160 nm* to ~500 nm
• Emitters can also be detectors
• Solid state light source
• Limited stacking faults, dislocations, and other crystal defects
• ZnO self-organizes into resonator cavities that readily emit laser light (on

silicon wafer coated with Ti and Au thin films)

Zeno Materials LLC

** As measured by Dr. David Look – Wright-Patterson AFB (USAF) & Wright State University.

Probabilistic Programming for Perceptually Driven Autonomous Agents

David Wingate

Perception, Control and Cognition Laboratory Computer Science Department wingated@cs.byu.edu

Learning and acting in complex domains

• Goal: autonomous agents that perceive, reason and act
• Agents must build models of the world that include
• object-based perception
• causality
• structured planning
• reasoning about other agents
• social cognition
• natural language semantics
• etc…
• How can we do this?
• … with code that is evolvable, scalable, maintainable and safe?

Probabilistic Programming and DNNs

We need to combine the strengths of two frameworks: Bayesian models and Deep Learning

DNNs are responsible for pixel processing Reasoning about other agents Higher-order perception Acting

Social Cognition

The principle of rationality: an agent tends to choose actions that she expects to lead to outcomes that satisfy her goals

Sally walks up to a vending machine. The machine has two buttons, A and B. A always dispenses chips. B usually dispenses cookies, but will sometimes dispense chips. Sally pushes button B, and gets a bag of chips. What did Sally want?

Goodman and Tenenbaum, ProbMods, retrieved 2016

Social Cognition

The principle of rationality: an agent tends to choose actions that she expects to lead to outcomes that satisfy her goals Inference must implicitly account for the counterfactual alternatives This sort of reasoning can be formalized with probabilistic models that combine beliefs, desires, goals and plans

Goodman and Tenenbaum, ProbMods, retrieved 2016

Social Cognition

Goodman and Tenenbaum, ProbMods, retrieved 2016

Summary

• Goal: autonomous agents
• Evolvable, scalable, maintainable, debuggable, explainable,

safe

• Must build models of the world
• Involving objects, agents, plans, decisions – uncertainty

everywhere!

• Integrated by virtue of the compositionality of hierarchical

Bayesian models

• Strategy: combine DNNs and Bayesian models
• Delivered as core capabilities of a probabilistic

programming language

• Drawing on models from cognitive science and ML

Thank you!

Computer Aided Proof, Stability of traveling waves

Blake Barker Department of Mathematics blake@mathematics.byu.edu

Computer Aided Proof

 Track all errors in a computation  Use high level mathematics  Find truncation error of method  Bound machine truncation error with intervals 

Stability of traveling waves

 Water waves  Detonation waves  MHD waves  Waves in gas dynamics  Microtubule formation

Roll Waves

Blake Barker Department of Mathematics blake@mathematics.byu.edu

Teaching Leadership in Teaching and Mentoring

Denise Halverson Mathematics halverson@math.byu.edu (801) 422-1207 Area of Interest:

Teaching leadership in teaching and mentoring

Collabora rators rs

Lon Cook

Chemical Engineering Ira A. Fulton College

Mike Diede

Exercise Sciences College of Life Sciences

Leadership

• “Management is doing things right;

leadership is doing the right things.”

• ~Warren Bennis
• “My definition of leadership is communicating

to people their worth and potential so clearly that they are inspired to see it in themselves.”

• ~Stephen R. Covey

Leadership

• Leadership is doing what is right, valuing others as children of
• ur Heavenly Father, and pursuing the greatest good for all by

applying correct principles.

How would you:

Counterfeit Leadership Leadership

Manage talent? Use Develop Approach mistakes? Blame Explore Set direction? Tell Challenge Make decisions? Decide Consult Get things done? Control Support

Why Implement Leadership Practices in Teaching and Mentoring?

Students feel empowered as life long learners. Students develop personal mission statements. Students learn and implement universal principles in their lives:

• Continuous learning
• Service
• Positive energy
• Viewing others as children of our Heavenly

Father

• Balance
• Excitement for life and its opportunities
• Self-renewal

Student learn how to be Christ-centered. Students are enabled to stand up and speak up for what is right, what really works, and the good of all.

MAA MathFest 2014 Boston, MA Summer Research Group, 2016 Authors

Leadership Topics

• BYU Mission
• Accountability
• Centeredness
• Mission Statements
• Beginning with the End in Mind
• Roles and Goals
• Multipliers
• Way of Being
• Levels of Awareness
• Social Influence
• Speed of trust
• Seek first to understand
• Coaching
• Think win-win
• Synergy
• Principle centered leadership

MAA MathFest 2015 Washington D.C.

Teaching Leadership in Teaching and Mentoring

• Meeting every other week to
• Learn leadership principles
• Discuss applications
• Share experiences
• Please let us know you are interested by providing your name and

email address to halverson@math.byu.edu. We will send out an email to get your availability and determine the best days and times to meet.

Nanomagnetism

Karine Chesnel Physics & Astronomy kchesnel@byu.edu (801) 422-5687 Areas of Interest:

Nanomagnetism in magnetic thin films and nanoparticles

Magnetic domain morphology in thin films

Maze pattern Bubble pattern Maze pattern Bubble pattern 10 µm images

• Studying the magnetic domain pattern morphology

and its dependence with previously applied field

Magnetic memory in thin ferromagnets

Magneti c Field Coherent x-rays synchrotron x-ray beam

c d e

Correlation maps

Low field cooling High field cooling

• Nature Communications, 7, 11648 (1 June 2016)

Magnetic ordering in nanoparticles

coherent x rays

tuned to Fe L3 edge

XMCD results:

• rbital moment ML quenched

spin MS reduced /bulk

0.1 0.2 0.3 0.4 0.5 0.6 0.7

• 0.8
• 0.6
• 0.4
• 0.2

0.0 0.2 0.4 0.6 0.8 1.0 1.2

3000 1000 600 400 200 100

• 100
• 300
• 400
• 600
• 800
• 1000
• 3000

Magnetic ratio q (nm

• 1)

Sample 2, 15 K Field study at 708 eV

Dependence of scattering signal with magnetic field

Experimental techniques

Magnetic Force Microscopy (MFM) Vibrating Sample Magnetometer (VSM) Synchrotron x-ray magnetic scattering