Andrew Kahng – October Layout Techniques for Cost- Driven Control of Lithography-Induced Variability Dennis Sylvester / Andrew B. Kahng U. Michigan EECS Dept. / UC San Diego ECE Dept.

Andrew Kahng – October 2001 Project Overview Objective: Develop cost models, design rules, layout methods, and tools to: (1) minimize costs associated with resolution enhancement techniques (RETs), and (2) limit lithography-induced variability Faculty –Dennis Sylvester, University of Michigan EECS Dept. –Andrew B. Kahng, University of California, San Diego ECE Dept. Students –Jie Yang, PhD expected 2006 –Puneet Gupta, PhD expected 2006 Mentors –Luigi Capodieci, AMD –Lars Liebmann, IBM –Karl Perrey, Intel –(Warren Grobman, Motorola)

Andrew Kahng – October 2001 RETs for Subwavelength Lithography WYSIWYG (layout = mask = wafer) failed starting with 350nm generation Optical lithography: feature size limited by diffraction Available knobs –aperture: OPC –phase: PSM –  RETs

Andrew Kahng – October 2001 Optical Proximity Correction (OPC)

Andrew Kahng – October 2001 Phase Shifting Mask (PSM) conventional mask glass Chrome phase shifting mask Phase shifter 0 E at mask 0 0 E at wafer 0 0 I at wafer 0

Andrew Kahng – October 2001 Mask Component of Manufacturing NRE Half of all mask sets used for < 570 wafers (< 100K parts) M. Rieger, Avant! – ISMT Mask-EDA Workshop July 2001 OPC, PSM, Fill  increased feature complexity Vector scan: Write cost proportional to feature complexity Difficult to inspect, verify masks!

Andrew Kahng – October 2001 P. Buck, Dupont Photomasks – ISMT Mask-EDA Workshop July 2001 Context-Dependent Fracturing Same pattern, different fracture

Andrew Kahng – October 2001 P. Buck, Dupont Photomasks – ISMT Mask-EDA Workshop July 2001 ITRS Maximum Single Layer File Size MEBES Data Volume (GB) Year

Andrew Kahng – October 2001 P. Buck, Dupont Photomasks – ISMT Mask-EDA Workshop July 2001 ALTA-3500 Mask Write Time ABF Data Volume (MB) Write Time (Reformat + Print) (Hrs)

Andrew Kahng – October 2001 RET Issues PSM and lithography equipment choices (e.g., OAI) constrain layout, explode mask costs –Forbidden configurations (wrong-way critical-width doglegs, or diagonal features) –Cost-reducing variants (IDEAL, GRATEFUL) more constraining Process window and yield enhancement: ditto –Forbidden width-spacing combinations (defocus window sensitivities) –Complex “local DRCs” OPC subresolution assist features: ditto –Notch rules, critical-feature rules on local metal Other –Dummy fill changes RCX results, creates inter-layer dependencies, increases data volume –halation rules (width- and length-dependent spacing)

Andrew Kahng – October 2001 Example: Off-Axis Illumination Different variants of off-axis illumination act to: –Amplify dense lines while attentuating larger pitches –Print only axis-parallel lines, not 45º lines F. Schellenberg, Mentor

Andrew Kahng – October 2001 RET-Related Needs in 2001 ITRS Function- and cost-aware OPC, PSM, dummy fill Real goal = predictable circuit performance and function Tools must understand functional intent –Make only corrections that gain $$$, reduce performance variation –Make only corrections that can be manufactured and verified –Fail only mask errors that affect function –Understand (data volume, verification) costs of breaking hierarchy Fix broken flows –Making same OPC corrections 3x (library, router, PV tool) –Layout signoff w/o comprehending later RET insertion (e.g., fill)

Andrew Kahng – October 2001 Key Question Which should be developed: (A) circuit design techniques that can deal with variability, or (B) process techniques that reduce variability? At < 65nm, variation-tolerant circuit techniques may be a more cost-effective way to combat rising process uncertainties Should seek better bang for the buck in applying RETs –Function-, cost-driven corrections –Synergies among various correction techniques (e.g., fill helps OPC)

Andrew Kahng – October 2001 Quantifying Impact of RETs Performance fluctuations due to dummy fill –Varying filling constraints, physical CMP models, tiling rules –Gate delay impact unclear –Grounded fill  inductive impact? MOSFET I-V characteristics (including I off ) with and without OPC/PSM –Varying proximity, layout configuration, gate dimensions Layout density and performance –PSM-compliant libraries trade density for “free composability” –Bent gates no longer available –Intermediate-pitch wiring may underperform due to variability increase stemming from lack of subresolution assist features

Andrew Kahng – October 2001 Investigating the Variability/Cost Relationship Initial driving question: How does the maximum value design compare to the maximum performance design? Maximum value is driven by parametric distributions as well as value distributions (e.g., bin sorting) What are the design implications for a value maximized design? –E.g., rely more heavily on spatial correlations or strive for more random behavior (averaging effects) ??? To measure $/wafer, we need (1) detailed models of process variability, and (2) models of how chip parameters (frequency, testability, etc.) affect value

Andrew Kahng – October 2001 Investigating the Variability/Cost Relationship (1) (A) Build a taxonomy of variability sources –Intrinsic vs. dynamic (Vdd,temp) –Random vs. systematic –Intra-die, Intra-reticle, Intra-wafer, etc. –Including correlations (local, global) (B) Incorporate a smart Monte Carlo approach –Based on performance sensitivity to sources of uncertainty (C) Build generic critical path models –Delay-constrained vs. power-constrained –+ Ring-oscillator, clock tree, etc. models

Andrew Kahng – October 2001 “Mapping Design to Value”: (1) Across-Wafer Frequency Variation

Andrew Kahng – October 2001 “Mapping Design to Value”: (1) Across-Wafer Frequency Variation

Andrew Kahng – October 2001 “Mapping Design to Value”: (2) Can we combine (1) and (2) to drive design optimizations?

Andrew Kahng – October 2001 “Mapping Design to Value”: (2) Can we combine (1) and (2) to drive design optimizations?

Andrew Kahng – October 2001 Research Timeline Year 1: Assess cost vs. variability tradeoffs, develop cost models for RETs Year 2: Develop layout methods and tools for cost-driven insertion of RETs while meeting performance goals Year 3: Model calibration/verification using test structures, circuit layout approaches to account for RET-based constraints

Andrew Kahng – October 2001 SPARE SLIDES

Andrew Kahng – October 2001 RET Resource Requirements Grobman, Motorola

Andrew Kahng – October 2001 RET Roadmap Rule-based OPC Model-based OPC Scattering Bars AA-PSM Weak PSM Rule-based Tiling Optimization-driven MB Tiling 0.25 um 0.18 um 0.13 um 0.10 um 0.07 um 248 nm 248/193 nm 193 nm Number Of Affected Layers Increases / Generation Litho CMP W. Grobman, Motorola – DAC-2001

Andrew Kahng – October 2001 Mask Data and the $1M Mask NRE Too many data formats –Most tools have unique data format –Raster to variable shaped-beam conversion is inefficient –Real-time manufacturing tool switch, multiple qualified tools  duplicate fractures to avoid delays if tool switch required Data volume –OPC increases figure count acceleration –MEBES format is flat –ALTA machines (mask writers) slow down with > 1GB data –Data volume strains distributed manufacturing resources Refracturing mask data –Before: mask industry never touched mask data (risky, no good reason) –Today: 90% of mask data files manipulated or refractured: process bias sizing (iso-dense, loading effects, linearity, …), mask write optimization, multiple tool formats, …

Andrew Kahng – October 2001 Process Variation Sources Design  (manufacturing variability)  Value Intrinsic variations – Systematic: due to predictable sources, can be compensated during design stage – Random: inherently unpredictable fluctuations and cannot be compensated Dynamic variations – Stem from circuit operation, including supply voltage and temperature fluctuations – Depend on circuit activity and hard to be compensated Correlations – Tox and Vth0 are correlated due to – Line width and spacing are anti-correlated by one; ILD and interconnect thickness also anti-correlated

Andrew Kahng – October 2001 Many Variability Sources… Example: Field-dependent aberrations cause placement errors and distortions Center: Minimal Aberrations Edge: High Aberrations Towards Lens Wafer Plane Lens R. Pack, Cadence