Solid State Lighting

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Solid State Lighting
Opto-Electronics Applications
From a torch to Blue and White LEDs and to Solid
State Lamps and to Microelectronics
Blue LED on Si, Courtesy of SET, Inc.
Photonic Links
Shur
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Importance of Solid State Lighting

• 21 of electric energy use is in lighting
• Half of this energy can be saved by switching to
  solid-state lighting sources
• Projected savings from solid-state lighting might
  reach 115 billion by year 2020
• Solid-state lighting expected to reach lifetimes
  exceeding 100,000 hours
• At present, LEDs are the most efficient sources
  of colored light in almost entire visible
  spectral range.
• White phosphor-conversion LEDs already surpassed
  incandescent lamps in performance

Challenges in solid state lighting Improve
efficiency of light generation Improve efficiency
of light extraction Improve quality of light
Reduce COST
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Optical Links will support massive data
transport and processing for future information
technology systems

• Optical links for
• Computer-to-computer (today)
• Board-to-board (2-5 years)
• Chip-to-chip (5-10 years)
• Optical interconnects within a chip (10-15 years)

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Multiscale Modeling Needs for Solid State Lighting
Nanoscale link quantum dots structure to light
extraction
Macro scale optimize Spectral Power Distribution
Micro scale link LED layer structure to light
extraction
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Multiscale Modeling Needs for Optical Links
Nanoscale VCSEL and photodetector modeling based
on first principles
Macro scale optimize light distribution in the
system
Micro scale link layer structure to light
extraction and detection
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Validation Experiments for Solid State Lighting
Measure efficiency and color rendering index
Measure luminescence intensity
Measure LED transient characteristics
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Validation Experiments for Optical Links
Measure circuit performance
Measure transient characteristics
Measure I-V characteristics
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Nano Peapod Silicon Cluster inside Carbon
Nanotube
Tuning the Optical Properties Nano Optical Device
Nano Engineering Design
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Predictive Process, Property, Performance Models
Reactor (dm)
Atomistic (Å)
Optimize design process setpoints to achieve
specified performance
Property Performance Models 2003-5
2002
IBM
2002
2000
Die (mm)
Island (nm)
2002
2000
Cale
Semi. Int., 1996
Feature Grain (0.1 1 mm)
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Property Performance Predictions
Property and performance predictions
Processing (integrated multiscale process
simulation)

• Compare computational results for structure
  against data.
• Use MD calculations locally, as needed, e.g.,
  to predict defect densities during grain
  structure simulations. (Project with H. Huang
  starting now.)
• MD basics are established. We are developing
  methods to link discrete and continuum models via
  a new homogenization procedure.

Huang
Huang
Established continuum mechanics
Borucki, Klass, Maniatty
Baumann
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Technology Transfer

• LEDs for general lighting
• UV LEDs for detection hazardous biological agents
• Special lighting for medical applications
• Fluorimeters
• Optical links for data transfer
• Optical interconnects
• Short range covert communications

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Optoelectronic Application Team

• Application and validation experts
• Tim Cale
• Michael Shur
• Multiscale engineering experts
• Tim Cale
• Hanchen Huang
• Saroj Nayak
• Mark Shephard
• Antoinette Maniatty
• Industries to be involved with tech-transfer
• Lucent
• GE Lighting
• SET
• Synergy with RPI
• Interaction with CIE
• Interaction with FRC
• Interaction with Center for Broadband Data
  Transfer