Theory and Modeling in Industrial Setting - Stategies, Failures and Overall Value Kirill Rivkin All...

Theory and Modeling in IndustrialSetting - Stategies, Failures and Overall Value

Kirill Rivkin

All expressed opinions are that of Kirill Rivkin personally. Due to general nature of this presentation, it is likely to contain overall generalizations and factual errors.

To be presented at University of Kentucky (UK)

Seagate Confidential

The Research Process

Stuart Parkin’s curve

Actually it’s just like a stock market

time

#of

Pub

licat

ions

Idea

Academic Research

Great bust

Industrial R&D


time

#of

Pub

licat

ions

Professor at UK comes up with an ideaProfessor at UK comes up with an idea

Big scientist publishes in Nature, ignores UK professorBig scientist publishes in Nature, ignores UK professor

Grant money pouringGrant money pouring

If you don’t do it, you are not a scientistIf you don’t do it, you are not a scientist

Government wants resultsGovernment wants results

Just two more years and we will cure AIDSJust two more years and we will cure AIDS

Big scientist publishes in Nature on a different subject.Big scientist publishes in Nature on a different subject.

Initial experiments were wrongInitial experiments were wrong

It’s a fun topic - you don’t really need a job?It’s a fun topic - you don’t really need a job?

Can it really work (Industry)?Can it really work (Industry)?


Context

• Various approaches to industrial R&D

• How different it is from Academia?

• The role of modeling and it’s derivatives.

• Technical question of industrial modeling.

• Collaboration with Academia.


R&D – Industry powerhouse• Facilities such as IBM Almaden, Bell Labs, Seagate

Pittsburgh, Microsoft Research Group (UCSB)

• High class Academic research with potential practical applications 10 years away.

• Does not work in long term.- Probability of big ideas becoming profitable is small- It takes many years to make it real, competitors may catch up;

there is no reason to be first.- R&D is staffed with people who do not have experience with the

current product.


Examples

• IBM – became a software company with highly limited, government sponsored research projects.

• Bell Labs – operates at 25% of its peak capability

• Seagate Pittsburgh – closed.

It’s a great advertisement for a high-tech company, showing investors its potential and technological leadership.


Practical R&DYears to product

5 4 3 2 1 0.5

What are the effects we can use? Their limitations? Basic physics? Can we build it?

What is the nature of failure modes?

What is a design for our roadmap?

Can it actually work?How much are we getting out of it?

How expensive is it?Can we actually make money?


Academia vs. IndustryAcademia Industry

One project at a time per contributor 5-6 projects at a time per contributor

3-4 years per project 1-7 months per project

Stay on the project until you gathered all the basic information

As soon as you understand how it works – move on!

Outcome – formula and paper. It is good if it works right away, but you can wait.

It has to work. In some form. Somehow. Experimental verification is the key

100’s of people on the same project Very few scientists trying to make a big impact

Can work on anything I want. If there are grant money for this.

Can work on anything I want. If there is a perception that it is important.

Can do basic research. Want to do basic research, but don’t have time

The more important I am, the more I publish

The more important I am, the less I can publish

Prescribed guidelines for salaries More aggressive promotion capabilities, generally higher salaries.


R&D areas of research• If the subject involves complex, quantum-mechanical

phenomena, a lot of theory and modeling should be used to understand it, right? Wrong!

• In the Industry attractiveness of hiring theorists exists only if experiments can’t offer good predictions.

• Examples:• Finance. Predicting the future experimentally is too expensive.• Magnetic media. Lots of physics, but takes 1< hour to conduct experiment.

Entire R&D is accomplished by massive DOE• Magnetic head. Takes 120 days to build – physical design is very important.• Optics – hard to investigate many properties on nm scale, reliable, well

established tools can help a lot.• First principles materials calculations – can be very useful, but not very

precise.


Moore’s law and job for physicists

Stable expansion by 40-50% each year is possible only if there are no physical or processing limitations and progress is driven by scaling and sigma reductions. Physics creates “jumpy” growth.

Process engineers job market

Modelers job market


Modeling strategiesPhysics modeling System modeling

Models selected, supposedly crucial, physical processes. Makes few approximations regarding the modeling methods. Uses well established equations, fine discretizations and well defined convergence criteria.

Does not model the “real world”. Makes strong assumptions, selecting one physical phenomena out of many. Relies on custom, modifiable tools.

Models all physical processes involved in a “real” system. Tries to include realistic dimensions.

The modeling methods are either very approximate, obscure or rely on (assumed) very rapid convergence. Typically uses a single, standard tool.

In every company there are two standards of modeling, followers of which tend to fight each other.


Practical exampleWhich one do you like more:

1. “With expected changes in average reflectivity of the Earth and given amount of radiation from the Sun, average temperatures will rise 1C over the next 30 years”.

2. “We modeled distribution of temperature on Earth for the next 30 years. On July 15th, 2020 the temperature in Pittsburgh will be 28.4 degrees, while it is being swept by 4.2 feet waves”.

The second one looks more attractive and detailed, but relies on correct representation of extremely complex physics (which is obscured from the reader). The first one is simple and straightforward, but relies on correct simplification of the problem (which is however not obscured).


The role of modeling

• There are fields where modeling succeeded in correctly predicting the future, providing insights and helping with inventions. Companies might support custom made software, expecting better predictability and performance.


How precise is the modeling?

• Typical answer to this question is that algorithm ABC converges to the answer with 0.1% error. It is, obviously, irrelevant.

• Modeling precision depends on knowledge of physical parameters – resistivity, permeability, saturation magnetization. All of them depend on exact chemistry, different for nanoscale (typical for modern high-tech) and sheet film. 10% is a good precision for less than crucial parameters.

• Since there are 5-6 of such parameters, fitting them will always produce a perfect correlation with the experiment.

• One rarely uses “book” algorithms as they are. Assumptions on boundary conditions, discretization and so on, will worsen the convergence.

Good model is qualitatively consistent in its predictions and quantatively precise within 10-15%.


Modeling – myths and realities

• In the industry we will deal with real tools, developed by generations of scientists. There are very few (mostly standard FEM) software packages that achieve widespread popularity and become widely used by community. Most software used is written by a few developers and then used by many.

• In the industry we will have dedicated model development activities. Industry appreciates the end result, not the model development. You should hide the latter among hours of “real” work. Unless you work for Mathworks

• The scope of problems in the industry is very limited and does not change.There are many more problems in the industry than people capable to solve them. There is always a choice – what and how do you want to work on. You have to be productive (or perceived as such), but in time you will have a big say in what you do. You can not spend a lot of time on your tasks, but you can work on very high-class, fundamental issues.

Exception – SPICE modeling and other examples of important, well established design simulations.


Scales of modeling

First PrinciplesFirst Principles Semi-classical and ClassicalSemi-classical and Classical SystemSystem

Used by most companies that deal with “advanced” materials, multilayers.Hard to compare with experiments. Can be very useful, but often makes mistakes.

Most common scale. Believed to be completely wrong by adherents of first principles. Relies on semi-classical equations.Treats each phenomena separately.Can be used in various fields (finance, mechanics, optics) etc.

Tries to predict performance metrics, specific for the given device.Combines a few phenomena together.Often uses interpolations.End purpose – creating a better design.Mostly used in very big companies, since such models are expensive and hard to implement.

Often one starts in one area and moves to another.


Technical notes

So what are the technical aspects of Industry’s computational modeling?

• We all stem from Academia, so you are likely to find similar tools to the ones you used as a grad student

• There is always going to be something solved with Finite Elements Method.

• There is always going to be a standard method – like FDTD for optics.

• There are always going to be people working with complex, custom made algorithms.

• Programming language – there are standards. C++ is good for finance, Fortran for magnetism.


Parallelization and hardware optimization

• Cluster optimization (MPI etc.) requires well distributed memory access and slow (>N2). Works well with a few problems.

• Multi-core optimization allows to linearly increase performance by a factor of 4. OpenMP is realistically one of the most popular optimizations.

• Hardware – most popular are FPGAs, GPUs and specialized accelerators.

• Most hardware accelerators have limited memory and operate via graphics bus. Can be bandwidth limited for N and NlogN algorithms.


Hardware optimization

• FPGAs:- Expensive to build (>10,000$). Work best with integers and

fixed dec. point representations. Most popular use – small scale FFT. Inflexible, but have very high performance.

• Accelerators:- Averagely expensive. Can come with a version of LAPACK

library. Easy to operate, have decent single precision capabilities. Work best with slow, complex algorithms that are included in LAPACK (matrix eigenvalues).

• GPUs:- Very inexpensive. Single precision. Hard to program

effectively, especially complex algorithms. Highly parallel for simple operations.


Modeling of hard drives.

Only the writing-reading head, channel/targeting algorithms and some mechanics are modeled extensively. The rest can be obtained by DOE.

But much of companies’ profit is made by producing best in class heads.


Magnetic WriterIs very simple. We just don’t know how it works.

Coil produces current, which magnetizes the writer and allows it to create strong magnetic field in the media, writing the information


How do we model it?

Parameter Model

Material’s magnetic properties First principles

Magnetization distribution on macroscale

FEM based on permeability models

Magnetization dynamics Micromagnetics based on semi-classical Landau-Lifshitz equation

Performance – basics (magnetic field and its gradient)

FEM and Micromagnetics

Performance – industry metrics (Bit Error Rate)

System models

Is all modeling equally useful? No, FEM and Micromagnetics offer by far the most precise predictions.


Collaboration with Academia

Postulate #1: Academia and Industry do not like each other

Ideal reasons for collaboration:

Academia can do extensive, long term, research into basic properties.

Often, the reasons for collaboration are:

1. Grad students are dirt cheap.

2. Prospective hiring high quality grad students.

3. Industry leaders want to publish more papers.

4. Personal friendship.

5. The idea is too crazy to develop it inside, but too attractive to just drop it.


But it can still be a great success

• Companies can know of some very unusual phenomena.

• Companies have access to multi-billion manufacturing equipment.

• Universities can do long term studies that are not product-oriented.

• It is easier (and cheaper) to contract a university than to build an internal capability.


Can I join the Dark Side?• You can’t just sell your soul to the Devil. Industry

requires different mentality than Academia.

• The first step usually is getting an internship.

• The more high level modeling/design you want to do, the fewer positions you’ll find out there. It is easier to grow into one from inside the company, than to have one created for you.

• Publishing papers is important, but it is more important to publish controversial, advanced studies on high level, potentially applied problems.

• Go to conferences and meet the people.

Theory and Modeling in Industrial Setting - Stategies, Failures and Overall Value Kirill Rivkin All...

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