NEMO5 on Blue Waters - A Flexible Package for Nanoelectronics - PowerPoint PPT Presentation

Network for Computational Nanotechnology (NCN) NEMO5 on Blue Waters - A Flexible Package for Nanoelectronics Modeling Problems Jim Fonseca Network for Computational Nanotechnology PRAC - Accelerating Nano-scale Transistor Innovation PI: Gerhard Klimeck Blue Waters Symposium May 2015

NEMO5 - Bridging the Scales From Ab-Initio to Realistic Devices Ab-Initio TCAD Approach: Goal: • Device performance with realistic • Ab-initio: • Bulk constituents extent, heterostructures, fields, etc. • Small ideal superlattices for new / unknown materials • Map ab-initio to tight binding Problems: • Need ab-initio to explore new (binaries and superlattices) • Current flow in ideal structures material properties • Study devices perturbed by: • Ab-initio cannot model non- • Large applied biases equilibrium. • Disorder • TCAD uses quantum corrections • Phonons 2

NEMO5 – A multiscale simulation tool for nanoelectronic modelling • Multiscale/multiphysics • Empirical tight binding • NEGF, DD, QTBM, EM • Electron core, k.p, mode space • Ohmic and Schottky contacts • Scattering optical and acoustic • Phonons • Strain models-VFF, Keating, Lazarenkova • External magnetic fields • Solves • Atomistic strain • Electronic band structures • Charge density • Potential 30nm • Current • 4-level MPI parallelization • bias, energy, momentum, space 3

NEMO5 and nanoHUB • Distribution and Support Group on nanoHUB.org » https://nanohub.org/groups/nemo5distribution » Source code, example, discussion forum, run NEMO5 on Purdue Resources • nanoHUB.org » 330,000 annual users » 4,200 resources (video lectures, presentations, tutorials, etc.) » 330 simulation tools » Nanoelectronics, nanophotonics, materials science, molecular electronics, carbon-based systems, Microelectromechanical systems » 4,200 resources (video lectures, presentations, tutorials, etc.) » NEMO5 T ools  Quantum Dot Lab  Crystal Viewer  Bandstructure Lab 4  …

Network for Computational Nanotechnology (NCN) Non-equilibrium Green's functions method: Non-trivial and disordered leads Yu He, Yu Wang, Tillmann Kubis, Gerhard Klimeck

Problem: assumption of periodic contacts in NEGF contradicts experiment semi-infinite periodic contacts. Common self- Source Drain energy methods Sancho Rubio, Non-periodic geometries transfer matrix But in the real world… http://www.electroiq.com/articles/sst /2010/12/iedm-reflections_.html Random alloy Roughness Periodic assumption contradicts realistic contacts S. Koenig et al, Appl. Phys. Lett, Vol. 104, pp. 103106, 2014 How to solve non-periodic contacts? Q. Liu, et al, IEDM p.229 2013 6

General Lead Method: problem & idea Problem:  No exact solution for semi-infinite systems unless periodicity assumed  Approximate solution  Physically correct  Numerically solvable for arbitrary contact structures CAP CAP J. Driscoll et al, Phys. Rev. B. Vol. 78, pp. 245118, 2008 Idea: extend complex absorbing potential (CAP) method  Non-periodic contact : Hamiltonian for explicit contact segments;  CAP serves as scattering : physical assumption of contacts;  Efficient, memory thin : converge within finite iterations; 7

Example: SiGe random alloy Si Si 0.5 Ge 0.5 Si device length Si 0.5 Ge 0.5 Si 0.5 Ge 0.5 Si 0.5 Ge 0.5 Example: 3x3nm Si 0.5 Ge 0.5 nanowire in sp3d5s* tight binding Device length 20nm and 6nm Results averaged over 50 samples Justification: With same effective alloyed disorder in contacts, expected transmission has weak dependence of device length Yu He, Yu Wang, Gerhard Klimeck, Tillmann Kubis, "Non-equilibrium Green's functions method: 8 Non-trivial and disordered leads” Appl . Phys. Lett. 105, 213502 (2014)

Example: SiGe random alloy Si Si 0.5 Ge 0.5 Si device length Si 0.5 Ge 0.5 Si 0.5 Ge 0.5 Si 0.5 Ge 0.5  Alloyed contact yield virtually device length independent transmission;  DOS of contacts match device better  less reflections of electrons; General lead approach works well for contacts with alloy randomness. 9

Example: SiGe random alloy 10 20 10 20 Si Si 0.5 Ge 0.5 Si cm -3 cm -3 device length 20nm Si 0.5 Ge 0.5 Si 0.5 Ge 0.5 Si 0.5 Ge 0.5 gate length 8nm Non-trivial 45% decrease contacts in on-current. critical in transport simulations 10

Network for Computational Nanotechnology (NCN) Bilayer Graphene: a Good candidate for Transistors? Fan Chen, Hesameddin Ilatikhameneh, Rajib Rahman, Gerhard Klimeck

Graphene Transistor ON/OFF < 10 • Graphene has a zero band gap • It has a good high ON current, but it can’t be turned off We need to achieve: • Large ON/OFF ratio needed in transistors (~10 5 ) • Small OFF current -> Low power consumption http://www.jameshedberg.com/img/samples/ 12 12 https://www.kth.se/en/ict/forskning/ickretsar/

Bilayer Graphene Bandgap vs. E-Field Bilayer Graphene Band Gap with D av 300 250 Band Gap [meV] 200 150 100 http://jarilloherrero.mit.edu/research/gated-bilayer-graphene/ 50 0 2 4 6 8 10 D av [V/nm] Bandstructure Small field Large field B and Gap Bilayer Graphene: Create a Band-Gap by Electric Field • Control band-gap by applying vertical electric field 13 13

NEMO5 – realistic atomic approach Tao. Chu, Prof. Zhihong Chen Purdue Daniel Mejia Challenge: • Matrix size ~ 64 million • Inverse, Eigenvalues ① Limitation from fabrication technique, orbitals short channel effect, gate leakage … 3.2 million ② Device size is typically 100nm (thick) x . . . . 200nm (long) x 20nm (wide) 3.2 million . . . . • 3.2 million atoms in simulation . . . . Matrix . . . . 20x20 14 14 http://chemwiki.ucdavis.edu/

Bilayer Graphene: Open band gap IdVg 3 TG 20 10 1 5 I(  A/  m) 2 ON/OFF = 100 BG 1 V BG = -1.75 V 0.5 V ds = 0.002 V Image courtesy Gianluca Fiori Fermi = 0 eV 2 Band Gap opens through the change of 0.2 Top Gate -1 -0.5 0 0.5 1 1.5 2 2.5 3 V TG (V) V TG = 3.6V V TG = 1.15V V TG = -1.4V 0.2 0.2 0.2 3 2 Band Edge [eV] 1 Band Edge [eV] Band Edge [eV] 0 0 0 -0.2 -0.2 -0.2 -0.4 -0.4 -0.4 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 15 15 x[nm] x[nm] x[nm]

Dynamic Band gap Physical structure Dynamic band gap: V TG |V BG | ↑ ⇒ E ↑ ⇒ E g ↑⇒ I ON /I OFF ↑ S D Back oxide V BG Ec V ds = 0.002 V E F,Source E F,Drain Fermi = 0 eV V BG Ev Band Gap modulated by back gate 16 16

NEMO5 – self consistent simulation Construct Hamiltonian 64 million x 64 million matrix Add Potential Determine energy grid V BG 64 M x 64 M Eigenvalue Convergence? Calculate charge density 64 M x 64 M Inverse Calculate Potential Hundreds of data points One I-V data point 17 17

iNEMO Group • PI: Gerhard Klimeck • 3 Research Faculty: Tillmann Kubis, Michael Povolotskyi, Rajib Rahman • Research Scientist: Jim Fonseca • 2 Postdocs: Bozidar Novakovic, Jun Huang • Students: Tarek Ameen, Robert Andrawis, James Charles, Chin-Yi Chen, Fan Chen , Yuanchu (Fabio) Chen, Rifat Ferdous, Jun Zhe Geng, Yu He , Yuling Hsueh, Hesameddin Ilatikhameneh , Zhengping Jiang, Daniel Lemus, Pengyu Long, Daniel Mejia Padilla, Kai • Ryan Mokos Miao, Samik Mukherjee, Harshad • Intel, Samsung, Philips, TSMC Sahasrabudhe , Prasad Sarangapani, Saima Sharmin, Yaohua Tan, Yui Hong (Matthias) Tan, Archana Tankasala , Daniel Valencia Hoyos, Kuang Wang, Yu Wang , Evan Wilson 18 18