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University of California • Berkeley • San Diego • Los Angeles
The Calculus of Clips
IMPACT Seminarintegrated modeling, process, and computation for technology
http://impact.berkeley.edu
Kameshwar PoollaMechanical Engineering
Electrical Engineering & Computer ScienceFebruary 04, 2008
IMPACT • 2
My commute to work
IMPACT • 3
My Office
IMPACT • 4
My commute home
IMPACT • 5
The Industry Team – Thanks!
IMPACT • 6
Deep thanks to our Champions!
Lars LiebmannFrank SchellenbergAndreas Kuelhmann, Zhenhai ZhuRoawen Chen, Albert WuRandhir Thakur, Klaus SchuegrafDave Kyser, Gerry Maloney, Mamoru Miyawaki, Ron Powell,Michael hart, Xin Wu,Victor Moroz, Dipu Pramanik, John Ennals, Bruno LaFontaine,Vivek Singh, Randy Simmons, John Lee, Shankar Sadasivan,Harry Levinson
IMPACT • 7
More Thanks!
Eric Geigerich handling all the research agreementsFarah Pranawahadi budgets, dealing with UC DiscoveryChang-Rui Yin technical support
Andy Neureuther wisdomCostas Spanos patienceTsu-Jae King youth & energy
IMPACT • 8
The Faculty TeamAlon, Elad EECS UCB Integrated System Design, Mixed-signal ICsChang, Jane ChE UCLA Plasma mechanisms, feature modelingCheung, Nathan EECS UCB Plasma modeling, diagnostics, surface intenractionsDornfeld, David ME UCB CMP modeling, mechanics aspects Doyle, Fiona MatSci UCB CMP modeling, chemistry effectsKomvopoulos, K. ME UCB Surface Polishing, NanomechanicsGraves, David ChE UCB Plasma modeling, diagnostics, surface interactionsGupta, Puneet EE UCLA DfM, Optimization, Variability AnalysisHaller, Eugene MatSci UCB Dopant and Self-Diffusion in Si and SiGe AlloysHu, Chenming EECS UCB Device Modeling, Variability AnalysisKahng, Andrew EECS UCSD DFY, DFM, algorithmsKing, Tsu-Jae EECS UCB Novel Electron DevicesLieberman, Michael EECS UCB RF sources and E&M plasma modelingNeureuther, Andrew EECS UCB Litho, Pattern Transfer, Modeling/SimulationPoolla, Kameshwar ME/EE UCB DFM, Modeling, Computation, Control, MetrologySpanos, Costas EECS UCB IC Process Metrology, Diagnosis and ControlTalbot, Jan CE UCSD Chemical-Mechanical Planarization
IMPACT • 9
Facts & Figures
Budget– Total $ 8.9 M– Industry Cash $ 4.1 M– State Matching$ 4.8 M
Major Equipment Donations– Centura 200 epitaxial tool from Applied Materials– EM Suite Simulation Package from Panoramic Technologies– Wafer/Mask processing credits: Spansion, SVTC, Dupont, Photronics
Personnel– 17 Faculty + 23 Graduate Students + 5 Post-docs + 3 Undergraduates
All research agreements in place– Terms are more favorable to our sponsors than under IMPACT
IMPACT • 10
Activities
Workshop – Save the date!– Wednesday April 09, 2008– AMD Commons 10am to 5pm– Plenary by one of industrial experts?– Feedback on how to get feedback
Bi-weekly Seminar – Run by Professor Tsu-Jae King– Begin with the new faculty: Alon, Kahngg, Hu, Gupta– Will also recruit experts from industry
Round-table Groups– To be arranged
IMPACT • 11
Activities …
Student Internships and Hiring– To be arranged
Recruiting more industry partners– Sister program – Micron, TSMC, UMC, Chartered, Qualcomm– Not all are State of California based, so we won’t use UC Discovery– So it may get complicated!– Will ask for approval from IMPACT sponsors first
Website and Wiki– Needs to be updated– Wiki my be an excellent way to share information, papers, ideas
Feedback: [email protected]
IMPACT • 12
Our Research
Five Inter-connected Research Themes:
Novel Technologies
Tsu-Jae King
CMP
Dave Dornfeld
Etch
Jane Chang
Lithography
Andy Neureuther
Design-Manufacturing Interface
Puneet Gupta
IMPACT • 13
Research Theme A – Litho
Objective Invent a range of approximate-but-fast models based on first principles for assessing manufacturing realities upstream in the design flow
Key Projects
Simulation of electromagnetic effects of mask edgesCompact models for through-focus modelingProcess parameter specific electrical devices and circuitsLitho-aware decomposition for double patterning
IMPACT • 14
Litho – People
Faculty: Neureuther, Spanos, Poolla
Students: 7 Graduate students (some on partial support)
Juliet (Holwill)Rubinstein
Lynn Wang Eric ChinMarshal Miller
Interconnect Delay
Lateral Int. RO Circuits
M ask EM Effects
DP & E-Test
Dan Ceperley
IMPACT • 15
Litho: Through-Focus fast-CAD
Imy CEIImy CEI
RealReal
0.5um Separation
PMF: 0.257
Zernike.txt
IFT
PatternMatcher
SPLAT
MaskLayout
Pattern(coma)
MatchLocation(s)
Aerial Image Simulator
Zernike.txt
IFT
PatternMatcher
SPLAT
MaskLayout
Pattern(coma)
MatchLocation(s)
Aerial Image Simulator
Defocus Spherical HO Spherical
(λ/NA) (λ/NA)(λ/NA)
(λ/NA) (λ/NA)
Coma HO Coma
Mask phases• yellow = 0°• green = 90°• red = 180°
Aberrations Polarization
Line End Shortening, with 2 line surround
0
20
40
60
80
100
120
140
-0.6 -0.4 -0.2 0 0.2 0.4 0.6
Focus, waves RMS
LES
(nm
) Dark Trim
90 Degree Trim
270 Degree Trim
Clear Trim
Att-mask 90o
edge effects
Pattern matching
Compact model though focus
Standard Cell Interactions
2D Lateral Weight DRC
Lateral Influence Functions
IMPACT • 16
Litho: Electrical Test Patterns & Circuits
CD shift with Defocus (OPC)
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
0 200 400 600 800 1000
pitch (nm)
CD
shi
ft fr
om n
omin
al (n
m)
20nm
40nm
60nm
80nm
Focus Sensitive Pitch
Focus Insensitive Pitch
Collaborative Platformfor DFM
Collaborative Platformfor DFM
Circuit Simulation
Transistor Modeling Process Simulation
Circuit Simulation
Transistor Modeling Process Simulation
65nm Testchips
Simulation
Parametric Yield Simulator
Solutions across disciplines rather than within disciplines
Cypress Poly Block
Cypress DDLI Block
Metal Active Contact
Corner Poly
Center Poly
Cypress Poly Block
Cypress DDLI Block
Metrology
Quasar OPC Poly
Annular OPC Poly
Cypress Poly Block
Cypress DDLI Block
Metal Active Contact
Corner Poly
Center Poly
Cypress Poly Block
Cypress DDLI Block
Metrology
Quasar OPC Poly
Annular OPC PolyDuPont
Mask
Cypress Wafer Fab. ON
ONONON
ON
Drain 1 Drain 2
Source 1 Source 2
Collaborative Platform for DfM
Screening for Focus Sensitive Candidates Focus Test Patterns
Multi-Student Test MasksNovel Leakage Testing
=+
Contact Pad Thin line of conductive material
Open circuit created when aberration present
Defocus = 0.02 Defocus = 0.2Defocus = 0.0 Defocus = 0.02 Defocus = 0.2Defocus = 0.0
On-Line Database Sim/Exp
IMPACT • 17
Research Theme B – DMI
Objective Increase design predictability, decrease manufacturing cost and yield ramp time
Leverage unexplored interactions between design and manufacturing
Build design-usable models of process and use them to analyze/optimize design
Key Projects
Variation modeling in BSIM compact modelsLeakage modeling, monitoring and optimization in
presence of variabilityImpact of variations on power of mixed-signal circuitsModeling and optimizing for pattern-dependent variations
in standard cell designsDesign-aware mask inspectionComprehensive chip-scale variability modeling
IMPACT • 18
DMI – People
Students– UCB: Kedar Patel, Yu Ben, Kun Qian, John Crossley + 1 TBD– UCLA: 2 TBD– UCSD: 1 TBD
Faculty– Puneet Gupta (UCLA)– Costas Spanos (UCB)– Elad Alon (UCB)– Chenming Hu (UCB)– Andrew Kahng (UCSD)– Kameshwar Poolla (UCB)
IMPACT • 19
DMI:Non-Rectangular Gate ModelingThree components– Poly gate imperfections: well-studied (SPIE’05)– Active rounding: first publication (ASPDAC’08)– Line-end shortening: first publication (DAC’07)– Line-end tapering: under progress (PMJ’08)
Key elements– Equivalent length/width models– Separate modeling for Ion and Ioff– Takes narrow-width effect into account– Modeling for overlay distributions
nominal w/ diffusion rounding delta (%)
leakage (nW) 138.69 83.49 39.8
fall 70.57 68.54 2.9clk→q(ps) rise 76.07 74.07 2.6
fall 20.43 18.08 11.5setup time (ps) rise 42.71 35.01 18.0
Case Study: DFF
IMPACT • 20
Research Theme C – Plasma
Objective Couple models at various scales to understand plasma-surface interaction and predict profile evolution
Key Projects
Develop fast algorithms to determine energy and angular distributions of all plasma species
Develop fundamental models for plasma-surface interactions
Develop predictable profile simulator for etch and deposition processes
IMPACT • 21
Plasma – People
Students/Post-docs – Alan Wu– Monica Titus– John Hoang– Postdocs: TBD
Faculty– Jane Chang– David Lieberman– David Graves Sponsors
Lieberman(Theory, PIC-MCC)
Graves(“Beam” and MD)Surface-scale experiments
Chang(Fluid and DSMC)Feature-scale experiments
Electron energy
deposition
Plasma species energy and angular
distributions
Ion and neutral fluxes
Molecular dynamics
Feature level profile evolution and control
IMPACT • 22
Plasma: Surface & Feature Scale ModelsParticle-in-cell, Monte Carlo collision (PIC-MCC)
Molecular dynamics (MD) simulations and beam experiments
Monte Carlo feature scale model coupling with reactor model
Energy and angle of all species
Fundamental surface reactions
Origin of surface evolution
Couple models at various scales to understand plasma-surface interaction and predict profile evolution
100 nm
100 nm
100 nm
Low DCLow Wb
High DCLow Wb
Low DCHigh Wb2 nm hole in Si etched with 200 eV CF2
+
RF
ESC
ZResist etched by 150 eV Ar+; VUV; at 100C
~ 1000 nm
Energy (eV) Energy (eV)Energy (eV)
10 mTorr 500 mTorr80 mTorr
Neutral energy distributions
Ion and hot electron density
IMPACT • 23
Plasma: Research Integration
X. Hua, et. al, J. vac. Sci. Technol. B 24, 1850 (2006)
193nm PR
248nm PR
LER issue for 193nm PR
Ion Angular and EnergyDistribution (Particle in Cell modeling: M. Lieberman)
Species Distribution (Reactor-Scale Modeling: (D. Graves and J. Chang)
Photoresist Reaction Mechanism (Beam Experiments: D. Graves)
Profile Evolution (Feature Scale Modeling: J. Chang)
• Define testbeds for research integration (LER, Gate Stack Etch, PVD ….. etc.)
++
++
n
nn
n
IMPACT • 24
Research Theme D – CMP
•
Objective Identify key influences of chemical and mechanical activity including the coupling” of CMP/polishing
Develop an integrated model of CMP material removal Verify model thru simulation and test, as a platform for model
based process optimizationKey Projects
Determine fundamental mechanics of the electro-chemical removal of material
Comprehensive model of CMP material removal (including pattern dependency, prior deposition processes, material inducedvariations etc)
Establish mechanical elements of CMP material removal via FEM (incl: pad, abrasive/slurry/device/surface interaction) Understand effects of slurry chemistry on abrasive agglomeration/dispersion and material nano-hardness Investigate material removal at nanoscale
IMPACT • 25
CMP – People
Students– Shantanu Tripathi, UCB (ME/MSE)– Seungchoun Choi, UCB (ME/MSE)– Huaming Xu, UCB (ME)– Moneer Helu, UCB, (ME, NSF support)– Adrien Monvoison, UCB (ME)– Robin Ihnfeldt, UCSD, (Chem Eng)
Faculty– Dave Dornfeld – Fiona Doyle– Jan Talbot– Kyriakos Komvopoulos
IMPACT • 26
Head
Platen
Pad
dow
n-
forc
e
slurry supply
rotation of wafer
head
Wafer 4-12”
Copper
Feature
pad asperity
abrasive particles
100nm-10µm
~1µm
1-10µmPad asperity
Abrasive
Pad/Wafer
Die
Feature/Asperity
Abrasive Contact
Let’s “Google” CMP…effects at different scales
IMPACT • 27
Pad asperity
Abrasive
Abrasive ContactLet’s zoom in…
Slurry agglomerationCharging issues in lapping
IMPACT • 28
Summary of Current Progress
Pad/Wafer (~m)
Die (~cm)
Asperity (~µm)
Feature (45nm-10µm)
Abrasive contact (10nm)
Data structure for capturing multiscale behavior: tree based
multi-resolution meshes
Integrated chemo-mechanical modeling of material removal
Pattern Evolution Model for HDPCVD STI
Pattern density
Chip Layout
Evolution
IMPACT • 29
Research Theme E – Novel Technologies
Objective Investigate advanced device designs, materials, and processes to enhance and reduce variability in bulk MOSFET performance
Develop prediction and abatement methods for systematic variations due to lithography, CMP, and etch processes
Key Projects
Advanced bulk MOSFET design (King Liu) 3-D strain engineering (Cheung)Diffusion in hetero-structures (Haller)Scatterometry-based parameter extraction for calibration
of OPC, CMP, and etch processes (Spanos)Optical metrology for in-situ process monitoring (Spanos)
IMPACT • 30
Novel Technologies – People
Students– X– Y– Z
Faculty– Tsu-Jae King – Eugene Haller– Nathan Cheung– Costas Spanos– Chenming Hu
31
IMPACT • 31
Segmented Bulk MOSFET for Reduced Variability
A multi-gate structure provides for improved control of short- and narrow-channel effects, and reduces STI-induced stress effects.Steep retrograde channel doping reduces VT variation due to SDF.The segmented bulk MOSFET combines these features to reduce variability in performance, while retaining compatibility with strained-Si, high-k/metal gate & active body biasing technologies.
•Continuum doping ID-VG
DR
AIN
CU
RR
ENT
[A/2
0nm
]
30
20
10
σVT = 27.1 mV
•Continuum doping ID-VG
σVT = 10.1 mVSegmented Bulk MOSFETPlanar Bulk MOSFET
30
20
10D
RA
IN C
UR
REN
T [A
/ 20n
m]
DR
AIN
CU
RR
ENT
[µA
/20n
m]
DR
AIN
CU
RR
ENT
[µA
/20n
m]
C. Shin et al. (UC Berkeley), to be published
100 atomisticsimulations
LG = 20nm
EOT = 0.9nm
VDS = 1V
IMPACT • 32
The Calculus of Clips
Speculative seminarWant: reaction, opinion, outrage opinions, and even outrage
Basic Assertion:Working with clips is more efficient and natural than with distances or rules
Pixel size 10nm Mask 20mm x 20mm 2e6 x 2e6 pixelsClip < 2µ x 2µ 2e2 x2e2 pixels
clip
mask
IMPACT • 33
The Opportunity
Industry is moving to regularization– Restricted design rules– Re-use of standard cells, design blocks– BRIX
Potential opportunities for clip calculus– OPC re-use– Faster DRC– Faster hot-spot detection and repair– Faster printability analysis– Fast mask fragmentation for multiple-patterning– Faster RCX
IMPACT • 34
Definitions
Clips– Could be non-rectangular– Standard cells, macros, etc …
– Core “Central” part of a clip– Context “Outer” part of a clip– Library Collection of clips
Core & Context depend on target application– Ex: DRC– Ex: OPC– Ex: Printability analysis
context
core
IMPACT • 35
Ex: DRC
Current Practice– DRC brick is 2,000 pages and exploding– Conventional geometric rule-based DRC at 22nm will be unmanageable
Alternatives: Work with clips not distances– Leverage the speed of pattern match– Produce library of good, bad, or graded patterns– Use library to detect and correct new design layouts
Open problems– Clip-based DRC– Hybrid Rule-Clip based DRC– Correction!– Redundancy removal in rule bricks
IMPACT • 36
Ex: DRC …
Core and Context– Core is the region that is DRC clean given the fixed Context– Cannot assure that Context is DRC clean because that depends on
Context(Context)
Use Case– Library of known DRC clean (in core) clips– In a mask M, use PatternMatch against library L– Can eliminate the core of every matched clip – Will have to do DRC on remaining areas
DRC Clean core
Context
IMPACT • 37
Ex: OPC re-use
Current Practice– OPC is a serious computational bottleneck– Computed many times in design flow– Changes upstream in design flow require re-doing OPC– Printed pattern is then simulated for timing, power, printability, etc
Alternatives: Work with clips– Leverage the speed of pattern match– Produce library of clips and corresponding OPC corrected cores– Use library to reduce OPC computations
Open problems– Respecting and exploiting hierarchy– Library generation – Library extension (for a core, under what other contexts should we use
the same OPC correction)
IMPACT • 38
Ex: OPC re-use …
Core and Context– Core is the region for which OPC corrections are known given the fixed
Context– Do not know OPC corrections in Context because that depends on
Context(Context)
Use Case– Library of clips with corresponding OPC corrections in core– In a mask M, use PatternMatch against library L– Can eliminate the core of every matched clip – Will have to do OPC on remaining areas
Context
known OPC in core
IMPACT • 39
Clip Metrics
What is a good metric on the space of clips?Difficult problemMust also extend to Alternating PSM, Attenuating PSMMetric must also be computable in the language of rectangles
Standard pixel based metrics fail:
Treats each pixel independentlyDoes not respect proximity
‖ci− cj‖2 = # ofdisageeing pixels
IMPACT • 40
Clip Metric depends on application …
Ex: Printability AnalysisWhen are two clips similar?– If the images in Silicon of the core of both clips are similar– Litho model– Clips– Projection onto core
Suggests that we need application dependent weightings– Exposure & Dose sensitivity analysis will have different weights
‖c1 − c2‖ = ‖ΠL(c1 − c2)‖
c1 c2L
Π
IMPACT • 41
Some Computational Problems
mask M , clip c, library
1. ExactMatch: Find all instances of c in M[done & commercialized]
2. RoughMatch: Find all sub-patterns p in M with
3. ExactTile: Tile M with core of clips drawn from library i.e. choose tiling to maximize Area(core union)
4. RoughTile: Tile M with core of clips drawn from library such that context of mask sub-pattern p andlibrary clip c are close:
‖p− c‖≤ ε
L = {ck}
‖p− c‖≤ ε
IMPACT • 42
Some computational problems …
There are other very interesting problems– Net-list covering by clips– Library generation
These are all problems in CS, with a twist– Must compute based on rectangles– Must respect hierarchy– Must work– Must work on huge problems
Algorithm Classes– Randomized– Adaptive
IMPACT • 43
Conclusions
Computation will be a new bottleneck– OPC will get even more computationally expensive– Design rules will become extremely complex – Interactions across features, between layers, among processes
Clip-based paradigms have good potential– OPC re-use– Faster DRC, RC extraction, printability analysis– Faster hot-spot detection and repair– Fast mask fragmentation for multiple-patterning
Many challenging problems– Metrics– Use cases– Fast Algorithms