# Presentation: 014214

## Genetically EngineeredNanoElectronic Structures(GENES)

**Gerhard Klimeck,**
**Carlos H. Salazar-Lazaro, Adrian Stoica, and Tom Cwik**
**Jet Propulsion Laboratory**
**California Institute of Technology**
**Pasadena, CA 91109**

**Outline:**
**NEMO genealogy and general features**
**Structural device optimization:**
**Massively parallel genetic algorithm package**
**Conclusion**

## NEMO Genealogy

**NEMO was developed under a government contract at Texas Instruments and Raytheon from 1993-1997**
**Theory**
**Roger Lake, Chris Bowen, Tim Boykin (UAH), GK**
**Graphical User Interface**
**Dan Blanks, GK**
**Programming Approach, Philosophy, and Prototypes**
**Bill Frensley (UTD), GK**
**Coding**
**Manhua Leng (UTD), Chenjing Fernando, Paul Sotirelis, Dejan Jovanovic, Mukund Swaminathan (UTA), GK**
**Experiments for verification**
**Ted Moise, Alan Seabaugh, Tom Broekaert, Berinder Brar, Yung-Chung Kao**
**NEMO is based on non-equilibrium Green functions, in an implementation that is novel.  The development of NEMO has benefited from the vast research on resonant tunneling diodes that had been done before the project.**

## Summary of NEMO Capabilities

**Interface / Users:**
**FAST and dirty design.  ****	****interactive**
**Comprehensive analysis (SLOW).  ****	****batch**
**Physics**
**Charging**
**Semi-classical self-consistency, quantum self-consistency**
**Scattering**
**Phonons, alloy disorder and interface roughness (1band)**
**Bandstructure**
**1, 2, 10 tight binding band models nearest and next nearest neighbor coupling.**
**Realistically long devices**
**Novel boundary conditions**
**NEMO can trade off CPU time and memory against a variety of models.**

## All of NEMO's Facets:Formalism, Physics, and Technology

**Approximately 250,000 lines of code at time of delivery**

## “Genetically Engineered” Nanostructure Devices

- Automated device synthesis and analysis using genetic algorithms.
- Material spectroscopy through genetic algorithm analysis.
**Justification:**
- Empirical Design (usual process) is sub-optimal. Complete design space search is unfeasible.=> Develop automated design tools.
**Impact:**
- Rapid nanotechnology device synthesis and development.
- Generation of novel devices.
**Approach:**
- Augment NEMO to analyze individual structures in parallel.
- Augment parallel genetic algorithm package (PGApack) to optimize and select desired structures in NEMO.

**“****Genetically Engineered” Nanostructure Devices**

- Proposed system architecture.
- Prototype is operable in batch mode.

## Basic Genetic Algorithm Development

**Genetic algorithm parameter optimization is based on:**
**Survival of good parameter sets**
**Evolution of new parameter sets**
**Survival of a diverse population**
**Optimization can be performed globally, rather than locally.**

**S1**
**S2**
**...**
**...**
**...**
**...**
**SN**

**S1’**
**S2’**
**...**
**Si’**

**Si’’**
**...**
**SN’**

**Fitness-Ranking**

**S1’**
**S2’**
**...**
**Si’**

**Si’’**
**...**
**SN’**

**Evolution**

**S1**
**S2**
**...**
**...**
**...**
**...**
**SN**

**S1’**
**S2’**
**...**
**Si’**

**Si’’**
**...**
**SN’**

**Fitness-Ranking**

**S1’**
**S2’**
**...**
**Si’**

**Si’’**
**...**
**SN’**

**Evolution**

**Combine Sets**

**Generation M**

**Generation M+1**

## Basic Evolution Operations

**Mutation may move parameters out of their original parameter range**
**E.g: original range [5,6,7,...100]****5 could be mutated to 4, or 3 ...**
**Mutation size may be random.**
**Multiple parameters may be mutated at the same step.**

**Crossover maintains the original parameters range.**
**Crossover explores different combinations of existing genes.**

**Each set (Si) consists of several parameters (Pj)**
**The parameters Pj can be of different kinds: real, integers, symbols, ....**

## Development of a Fitness Function

**Eliminate series resistance effects**
**Eliminate NDR oscillations (step-like feature)**

**Fitness from of 6 error contributions:**
**Peak and valley **
**Slope at peak and valley**
**Overall absolute and relative error**

## First Simulation Results: Structural Analysis

**Employ parameterized non-parabolic single band model with full quantum charge self-consistency and transverse momentum integration.**
**Developed fitness function for typical RTD I-V curves. Need to shoot for peak position and amplitude, slope at peak and relative and absolute errors.**

**Allow genetic algorithm to vary 5 different structural parameters:**
**3 Thicknesses: well, barrier, spacer**
**2 Dopings: low doped spacer, unintentional doping in center**

## First Simulation Results: Structural Analysis

**Analyzed two similar InGaAs/InAlAs structures :**
**RTD 1: N1=1e18/cm****3****,****	****D2=1e15/cm****3****,****	****T1=7ml, ****	****T2=16ml, ****	****T3=16ml**
**RTD 2: N1=1e18/cm****3****,****	****D2=1e15/cm****3****,****	****T1=20ml, ****	****T2=16ml, ****	****T3=16ml**

## Resonance State Lowering due to Band Non-Parabolicity

**Resonance State Lowering due to ****Band Non-Parabolicity**

- Second diode turn-on at lower voltages.

**Dispersion E(k)**

**Resonator**

## Wave Attenuation in Barriers

**Wave Attenuation in Barriers**

- Attenuation is smaller with coupled bands
- Tunneling probability increases
- Current increases

## Future Interest

**Analyze material parameter influence on overall device performance****-> material spectroscopy**
**Implement general architecture such that a variety of different simulation tools can be plugged into an optimization tool.**
**Explore other optimization algorithms, such as simulated annealing or directive approaches within the same framework.****-> scripting tools that can link different tools****-> Tcl/Tk**