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When do new technologies become economically feasible?

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These slides use supply and demand curves and other theories to analyze when new technologies become economically feasible. Changes in the supply and to a lesser extent the demand curve gradually …

These slides use supply and demand curves and other theories to analyze when new technologies become economically feasible. Changes in the supply and to a lesser extent the demand curve gradually enable new technologies to exceed minimum levels of performance and fall below maximum levels of price. The chances of this occurring in the near future depend on the extent of improvements necessary and the rates of improvements. Rapid rates of improvement, which some technologies exhibit, enable new technologies to more quickly become economically feasible. We can use rates of improvement and the minimum thresholds of performance and maximum thresholds of price to estimate when new technologies become economically feasible. This is facilitated by the rather straight lines that performance vs. time curves exhibit, the important effect of R&D on cost and performance (more important than production), and other "realities" that this paper presents.

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  • 1. A/Prof Jeffrey Funk Division of Engineering and Technology Management National University of Singapore For information on other technologies: see http://www.slideshare.net/Funk98/presentations or Exponential Change: What drives it? What does it tell us about the future? http://www.amazon.com/Exponential-Change- drives-about-future-ebook/dp/B00HPSAYEM/ref=sr_1_1?s=digital-text&ie=UTF8&qid=1399871060&sr=1-1&keywords=exponential+change
  • 2. Session Technology 1 Objectives and overview of course 2 When do New Technologies Become Economically Feasible? 3 Two types of improvements: 1) Creating materials that better exploit physical phenomena; 2) Geometrical scaling 4 Future of ICs and Electronic Systems 5 Internet of Things, Sensors, MEMS and Bio-electronics 6 Chinese New Year, no class 7 Lighting, laser diodes, and Displays 8 Human-computer interfaces (also roll-to roll printing) 9 IT and Transportation 10 DNA sequencing and solar cells 11 Feedback on Group Slides 12-13 Group Presentations This is Second Session of MT5009
  • 3. Outline  Science, technology, innovation, and economic feasibility  Supply and demand curves and economic feasibility  Myths and realities about technology change (i.e., supply curve)  #1: Performance vs. time curves resemble an S-curve  #2: Slowing rate of improvement in old technology drives development of new technology  #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  #4: Costs fall as cumulative production rises in learning curve  #5: All technologies have the potential for rapid rates of improvements  An analysis of breakthrough technologies predicted by MIT’s Technology Review
  • 4. Science, Technology, and Innovation: Different People, Different Terms  Research (Basic, Applied) and Development  Science, Technology, Commercialization  Invention (proof of concept), Innovation (commercialization of discontinuity/concept), Diffusion of discontinuity/concept  Technological Discontinuity: based on new concept that comes from advances in science (sometimes called radical or disruptive innovation)  Scientific, technical, and economic feasibility  In any case, technologies proceed through stages of scientific, technical and economic feasibility  advances in science often continue throughout these stages and contributes towards improvements
  • 5. Technological Discontinuities  This term is widely used in courses on technology management  Disruptive innovations are one type of discontinuity  One reason discontinuities are discussed is because  Incumbents often fail to effectively commercialize them and thus lose substantial market share in the new technology  Thus discontinuities represent large opportunities for new entrants  It is also important to understand the concept that forms the basis for the technology  One reason is because this helps us understand the potential for improvements
  • 6. Steam-powered fire engine Technological Discontinuities: What was change in concepts? Old Technology New Technology, i.e., Discontinuity Early Benz (1894) Wright Brothers (1904) Gliders (19th Century)
  • 7. We Want to Know About these Discontinuities Before they are Introduced  To do this, we can think of the terms introduced on previous slides as different stages over time  Research (Basic, Applied) and Development  Science, Technology, Commercialization  Invention (proof of concept), Innovation (commercialization of discontinuity/concept), Diffusion of discontinuity/concept  Scientific, technical, and economic feasibility  Diffusion is often the last step and depends on economic (in addition to scientific and technical) feasibility
  • 8. The Last Stage is Diffusion and Within Diffusion there are many Stages
  • 9. Diffusion and Economic Feasibility  How can we better understand economic feasibility and timing of diffusion?  When might a technology become economically feasible?  When might it begin diffusing?  Or when might it diffuse to a larger number of customers?  We can distinguish between economic feasibility and the organizational and regulatory challenges of implementing new technologies  Answers depends on a variety of factors  But first we must think about  Supply & demand curves and  five myths of technology change
  • 10. Outline  Science, technology, innovation, and economic feasibility  Supply and demand curves and economic feasibility  Myths and realities about technology (i.e., supply curve) change  #1: Performance vs. time curves resemble an S-curve  #2: Slowing rate of improvement in old technology drives development of new technology  #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  #4: Costs fall as cumulative production rises in learning curve  #5: All technologies have the potential for rapid rates of improvements  An analysis of breakthrough technologies predicted by MIT’s Technology Review
  • 11. Quantity (Q) Price (P) q p What do Demand and Supply Curves Mean and what do they have to do with Diffusion? Demand Supply
  • 12. What are some problems with last Slide?
  • 13. What are some problems with last slide?  Previous slide assumes performance is unimportant  In reality, performance is important  Market evaluates products and services in terms of price and a variety of performance dimensions  Difficult to represent multiple dimensions on a two- dimension graph, so most graphs only show price and quantity  Let’s consider performance, which assumes price is held constant
  • 14. Quantity (Q) Performance (P) q p In terms of performance, What do Demand and Supply Curves Mean and what do they have to do with Diffusion? Supply Demand
  • 15. Price, Performance, and Demand  Price and performance determine the amount of demand and supply  Rising performance often leads to growing demand  Falling price often leads to growing demand  What changes over time and how do these curves look before there is a market (i.e., no commercial production)?  When performance is too low?  Or when price is too high?  How can we represent these dynamics with supply and demand curves?
  • 16. Quantity (Q) Price (P) q p Diffusion often starts in segments/users that are willing to pay more for products and services than are other segments/users Demand Curve Supply Curve Typical movement of supply curve over time Typical movement of demand curve over time
  • 17. Quantity (Q) Price (P) q p Maximum Threshold of Price: the maximum price that the market will pay for a new technology Demand Curve Supply Curve Typical movement of supply curve over time Typical movement of demand curve over time
  • 18. Quantity (Q) Performance (P) q p Sometimes, diffusion starts in segments/users that have lower performance expectations than other segments/users Supply Curve Demand Curve Typical movement of supply curve over time? Typical movement of demand curve over time?
  • 19. Quantity (Q) Performance (P) Minimum Threshold of Performance: the minimum performance the market will accept for a new technology Supply Curve Demand Curve Typical movement of demand curve over time Typical movement of supply curve over time
  • 20. Whether we Focus on Performance or Price  Demand and supply curves help us think about important issues  Impact of falling price or increasing performance on demand  Levels of performance and price that are needed before a technology becomes economically feasible  Other factors impact on diffusion such as standards, regulations, and organizational issues  Demand and supply curves can also help us to think about the first  Products to diffuse  First value propositions  First designs  Markets to accept this diffusion  First customer segments  First customers within segments  First sales channels
  • 21. But What Drives Changes in Demand and Supply Curves?  Demand Curves  Increases in income  Changes in competing and complementary technologies  Changes in consumer preference  Supply Curves  Predominant viewpoint is demand drives improvements on factory floor and thus changes in supply curves  But this ignores the reality of R&D  universities and other labs improve technologies long before the technologies are commercially produced  Some systems are heavily impacted on by component technologies  Lets understand the myths and realities of technology change
  • 22. Outline  Science, technology, innovation, and economic feasibility  Supply and demand curves and economic feasibility  Myths and realities about technology (i.e., supply curve) change  #1: Performance vs. time curves resemble an S-curve  #2: Slowing rate of improvement in old technology drives development of new technology  #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  #4: Costs fall as cumulative production rises in learning curve  #5: All technologies have the potential for rapid rates of improvements  An analysis of breakthrough technologies predicted by MIT’s Technology Review
  • 23. Time Performance Myth vs. Reality of Performance vs. Time Curves on Logarithmic Scale Slowdown and Limits Acceleration Time Performance (logarithmic scale) a. The Myth b. The Reality Note: limits exist but they are often further away than ordinarily thought
  • 24. The Theory for Purported S-Curves  Improvements accelerate as research funds moved from old to new technology in response  to increases in demand for new technology  or to slowdown in rate of improvement in old technology (Foster, 1986; Garcia and Clantone, 2002; Utterback, 1994)  Acceleration may also occur as technology is better understood by scientists and firms, constraints are overcome, and complementary technologies developed and implemented (Butler, 1988)  For later part of purported S-curve, rates of improvement slow as cost of marginal improvements increases and natural limits emerge  research funds then move to still newer technology and thus acceleration in newer technology’s rate of improvement (Foster, 1986; Butler, 1988; Utterback, 1994)
  • 25. S-Curves make it easy to fall for hype
  • 26. Let’s look at some real data Mostly straight lines on a logarithmic plot but with some deviations
  • 27. 0.01 0.1 1 10 100 1965 1975 1985 1995 2005 b. Lumens per Dollar (RedLEDs) vs. Time 0.001 0.01 0.1 1 10 1940 1960 1980 2000 2020 c.KwHours per Dollarvs. Timefor CrystallineSilicon Solar Cells 10 100 1000 2002 2006 2010 2014 e. Current (Amps) x Length (meters) vs.Time for YBaCuO Superconductor 100 1000 1985 1990 1995 2000 d. Energy (Joules)Per Volume (cc)vs. Time for Li-ionBatteries 0.001 0.1 10 1000 1960 1980 2000 2020 White Red a.Lumens per Packagefor LEDs vs.Time 0.1 1 10 100 1970 1980 1990 2000 2010 c. EfficiencyofAmorphous Siliconand OrganicSolar Cells vs.Time Amorphous Silicon Organic Limit? Slowdown?
  • 28. 0.001 0.01 0.1 1 10 1980 1990 2000 2010 2020 h. Millions of Pixels per Dollar vs.Time for Camera Chips 1 10 100 1985 1990 1995 2000 2005 2010 i.Light Sensitivity(mV/sq micron) vs. Time for CameraChips 0.001 0.01 0.1 1 1985 1995 2005 j.1/Pixel Size (sqmicron) vs.Time for Camera Chips 0.0000001 0.00001 0.001 0.1 10 1980 1990 2000 2010 k. Mobility (cm2/Volt-sec)of Organic Transistors vs.TIme 0.000001 0.0001 0.01 1 100 1950 1970 1990 2010 Micro- processors DynamicRandom AccessMemory (DRAM) MOS Logic f. Millions of Bits per Chipvs. Time 0.0001 0.01 1 100 1950 1970 1990 2010 DynamicRandom Access Memory Flash Memory g.Millions of Memory Bits/Dollar vs. Time Slow down?
  • 29. 1.E+01 1.E+04 1.E+07 1.E+10 1.E+13 1.E+16 1930 1950 1970 1990 2010 l.Computations/kwhour vs. Time 1E-09 0.000001 0.001 1 1000 1940 1960 1980 2000 m. Thousands of Computations/ Second/Dollar vs.Time 0.00001 0.001 0.1 10 1000 1970 1990 2010 n. CTScanner: 1/(Scan Time x Resolution) vs.Time 0.001 1 1000 1000000 1950 1970 1990 2010 o. Millions of BIts per Sqare Inch vs. TIme for MagneticDisks 0.1 10 1000 100000 1950 1970 1990 2010 p. Millions of Bits per Volume (cc) vs. Time forMagnetic Tape 0.001 0.1 10 1000 1950 1970 1990 2010 q. Millions of BIts/Dollarvs.Time forMagnetic Tape Slowdown? Acceleration? Acceleration? Acceleration?
  • 30. 0.001 0.1 10 2001 2003 2005 2007 2009 2011 2013 PhaseChangeRAM Ferro-electric RAM Magnetic RAM r.Storage Capacity(GB)per Memory Chip vs.TIme 0.001 0.01 0.1 1 10 1965 1970 1975 1980 1985 s.Distanceper Loss (km/decibel) vs.Time for OpticalFiber 0.1 10 1000 100000 1980 1990 2000 2010 t. LastMileBandwidth (1000s of bits/sec)vs.Time 0.01 0.1 1 10 100 1000 1980 1990 2000 2010 u. Millions of Bits per Second vs.Time for Cellular Telecom 0.0001 0.01 1 100 10000 1000000 100000000 1970 1980 1990 2000 2010 v. Sequenced BasePairs per Dollarvs. Time Acceleration Acceleration?
  • 31. 100 1,000 10,000 100,000 1920 1930 1940 1950 1960 1970 1980 y. Aircraft PassengerMiles per Hour vs.Time 1980 1990 2000 2010 0.1 1 10 2000 2005 2010 x. Output(liters)perDollar vs.Timefor Cellulosic Ethanol 1970 1980 1990 2000 2010
  • 32. No Evidence for an S-Curve  None of the 32 time-series curves display classical S- curve  Second half of S-curve, i.e., limits,  only evident in one technology, best laboratory efficiency of amorphous silicon solar cells (Figure 1.c), and perhaps a few others  However, output (kwHours) per dollar is probably still rising  similar to output per dollar for crystalline silicon solar cells (See Figure 1.c.). Another data base (Economist, 2012) shows continued reductions in cost beyond 2003 (Nemet, 2005), which is last data point Figure 1.c
  • 33. No Evidence for an S-Curve (2)  First half of S-curve, i.e., acceleration, only evident in one technology, cellular telecommunications (Figure 1.u)  This acceleration is expected since cellular phones were first used for voice communication  data speeds only became important in late 1990s as displays reached levels necessary for data speeds to become important; this explanation is consistent with the theory of S- curves (Foster, 1986; Butler, 1988; Utterback, 1994)  Better measure of performance for early years of cellular phones would probably be number of voice conversations possible per unit of spectrum  These data are in one of my supplementary files on the IVLE (and slideshare)  Not enough data points for statistical analysis, but straight line
  • 34. No Evidence for an S-Curve (3)  Several other curves deviate from straight line  Slowdowns  Mobility of organic transistors  Computer tomography  But this doesn’t mean limits will soon be reached since other technologies have seen slowdowns followed by accelerations  Magnetic recording density of tape and disks (due to introduction of giant magneto resistance)  Number of sequenced DNA base pairs - due to introduction of new technology (454, Illumina)
  • 35. Reality  Most curves more closely resemble a straight line on a logarithmic plot than an S-curve  What does a straight line on a logarithmic plot mean?  Why do I use log plots and not linear plots?  Let’s look at the statistical analysis  Linear model  Logarithmic model  Logarithmic model with time squared
  • 36. 0 100 200 300 400 500 1970 1980 1990 2000 2010 Millions of Transistors per Microprocessor: 1971 - 2007 With Linear Plots: Moment of Acceleration Depends On Last Data Point Plotted 0 2 4 6 8 1970 1980 1990 2000 Millions of Transistors per Microprocessor: 1971 - 1997 0 0.04 0.08 0.12 0.16 1970 1975 1980 1985 Millions of Transistors per Microprocessor: 1971 - 1982
  • 37. Technology Dimensions of measure Number of Data Point Linear Model Log. Model Log Model with Time2 Term R-Sq. P-Value R-Sq. P-Value R-Sq. P-Value Sign of T2 Light Emitting Diodes (LEDs) Lumen/package, red 15 .29 .02 .98 <.0001 .98 .38 Positive Lumen/package, white 7 .24 .15 .93 <.0001 .97 .13 Positive Lumens per dollar, red 14 .58 <.0001 .92 <.0001 .91 .95 Positive Si Solar Cells kwH per Dollar 47 .74 <.0001 .99 <.0001 .99 <.0001 Negative Organic Cells Efficiency 9 .94 <.0001 .98 <.0001 .98 .15 Positive Amorphous Si Efficiency 17 .83 <.0001 .56 .0003 .99 <.0001 Negative Superconducting Cables Current x length- YBaCuO 11 .77 .0002 .97 <.0001 .98 .07 Negative Li-ion Batteries Energy per volume 11 .93 <.0001 .91 <.0001 .90 .59 Negative Logic Chips Number of transistors per chip 10 .66 .003 .98 <.0001 .98 .3 Negative Microprocessors 21 .34 .003 .99 <.0001 .99 .2 Positive Dynamic RAM 13 .36 .02 .997 <.0001 .997 .54 Positive Bits per dollar 40 .36 <.0001 .99 <.0001 .99 .02 Positive Flash Memory 15 .37 .001 .96 <.0001 .99 .001 Positive Camera chips Pixels per dollar 31 .63 <.0001 .99 <.0001 .99 .04 Negative Light sensitivity 13 .79 <.0001 .99 <.0001 .99 .99 Negative 1/Pixel size 13 .77 <.0001 .99 <.0001 .99 .6 Positive Organic Transistors Mobility 9 .68 .004 .95 <.0001 .97 .03 Negative Computers Instructions/time/price 73 .07 .01 .93 <.0001 .93 .015 Positive Computations/kw-hr 66 .11 .004 .98 <.0001 .98 .78 Negative Computer Tomography 1/(scan time x resolution) 13 .68 .0003 .74 <.0001 .92 .00005 Negative Magnetic Disks Areal density 28 .19 .01 .95 <.0001 .97 .00002 Positive Regression Analysis of Performance and Cost vs. Time for Various Technologies
  • 38. Technology Dimensions of measure Number of Data Point Linear Model Log. Model Log Model with Time2 Term R-Sq. P-Value R-Sq. P-Value R-Sq. P-Value Sign of T2 Magnetic Tape Mbits per volume 14 .43 .005 .92 <.0001 .96 .003 Positive Mbits per cost 14 .167 .08 .85 <.0001 .93 .002 Positive Ferro- electric RAM Storage Capacity 9 .63 .007 .54 .014 .74 .11 Negative Magneto RAM Storage Capacity 8 .86 .002 .82 .003 .84 .26 Negative Phase Change RAM Storage Capacity 7 .44 .04 .9 .0002 .88 .71 Negative Glass Fiber Distance/decibel loss 8 .77 .003 .91 <.0001 .99 .001 Negative Last Mile Wireline Bits per second 8 .60 .02 .99 <.0001 .99 <.0001 Negative Wireless, 100 meters (cellular) 12 .11 .16 .86 <.0001 .91 .03 Positive DNA Sequencer Base pairs per dollar 11 .19 .026 .85 <.0001 .96 <.0001 Positive Cellulosic Ethanol Output per cost 11 .97 <.0001 .93 <.0001 .97 .57 Positive Aircraft Number of Passengers Times Speed 12 .81 <.0001 .97 <.0001 .99 .002 Negative Regression Analysis of Performance and Cost vs. Time for Various Technologies (continued)
  • 39. Why aren’t there Early Accelerations?  Improvements build from past improvements and the extensions to the knowledge base that these improvements bring  Thus, rates of improvement are relatively constant over time  R&D is decentralized and it has become even more decentralized over last 50 years  most researchers create their own research plans and try to publish something new and different  funding for new technologies is also highly decentralized  Thus, research efforts quickly move to new technologies as research results are presented and published and accelerations don’t occur  R&D spending increases over time as sales grow
  • 40. Outline  Science, technology, innovation, and economic feasibility  Supply and demand curves and economic feasibility  Myths and realities about technology (i.e., supply curve) change  #1: Performance vs. time curves resemble an S-curve  #2: Slowing rate of improvement in old technology drives development of new technology  #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  #4: Costs fall as cumulative production rises in learning curve  #5: All technologies have the potential for rapid rates of improvements  An analysis of breakthrough technologies predicted by MIT’s Technology Review
  • 41. Myth and Reality  Myth  Slowdown in rate of improvement for old technology causes new technology to be developed and improved  Reality  New technologies are being developed long before the rate of improvement slows in the old technology  Often multiple technologies are being simultaneously developed as replacements to an old technology  The following slides show examples of this
  • 42. Time Performance Myth vs. Reality of Slowdowns Driving Improvements in New Technologies Slowdown Acceleration Time Performance (logarithmic Scale) a. The Myth b. The Reality Many technologies are simultaneously being developed in a very decentralized manner and their timing depends on supply-side factors
  • 43. Technology Domain Technology Dimension of Measure Time Period Improvemen Rate Per Ye Integrated Circuits CMOS Microprocessor 1/Clock Period 1986-2012 24% Super-conducting Josephson Junction 1990-2010 20.3% Carbon Nano -Tubes for Transistors Purity of Carbon Nano - Tubes 1998-2012 52% Non- Volatile Memory Flash memory Storage capacity 1992-2013 50% Resistive RAM 2006-2013 272% Ferroelectric RAM 2001-2009 37.8% Magneto RAM 2002-2011 57.8% Phase Change RAM 2004-2012 63.1% Computing Digital Instructions per unit time 1947-2009 50% Quantum Number of Qubits 2002-2012 107% Telecom Last Mile Wireline Bits per second 1982-2010 48.7% Cellular 1996-2013 79.1% LAN 1995-2010 58.4% WLAN 1996-2008 77.8% Electricity Nuclear $/kw of capacity 1950-1983 -2.7% Fossil Fuel Watt-Hours per Dollar 1892-1972 5.5% Crystalline Silicon Solar 1957-2003 15.9% Efficiency 1957-2001 2.2% Lighting Fire Luminosity per Watt 1855-1917 3.8% Incandescent 1882-1948 2.2% Fluorescent 1950-2002 0.5% LEDs Lumens per package, red 1965-2008 16.8% OLEDs Luminosity per Watt, green 1987-2005 29% Displays LCDs Square meters per dollar 2001-2011 11.0% OLEDs Luminosity per Watt, green 1987-2005 29% Quantum Dots External Efficiency, red 1998-2009 36.0% Rates of Improvements for Technologies that Might be Considered Substitutes
  • 44. 0.01 0.1 1 10 1001.00E+06 1.00E+08 1.00E+10 1.00E+12 1975 1985 1995 2005 2015 Integrated Circuits CMOS Microprocessors Speed (Hz) Superconducting Josephson Junctions (Speed) Carbon NanoTubes (% impurities) % Impurities
  • 45. 0.001 0.01 0.1 1 10 100 1000 1990 1995 2000 2005 2010 2015 Did Slowdown in NAND Flash Memory Lead to Improvements in New forms of Non-Volatile Memory? NAND PRAM FeRAM MRAM RRAM Resistive RAM NAND Ferromagnetic RAM Phase Change Memory Magnetic RAM Billions of Bits
  • 46. 1 10 100 1000 1.00E+00 1.00E+01 1.00E+02 1.00E+03 1.00E+04 1.00E+05 1.00E+06 1.00E+07 1.00E+08 1.00E+09 1.00E+10 1.00E+11 1.00E+12 1.00E+13 1.00E+14 1.00E+15 1.00E+16 1940 1960 1980 2000 2020 Did Slowdown in Digital Computers Lead to Improvements in Quantum Computers? ComputationsperkwH NumberofQubits Quantum Computers (number of Qubits) Digital Computers (computations Per kwH)
  • 47. 0.1 10 1000 100000 10000000 1979 1984 1989 1994 1999 2004 2009 2014 Did Slowdown in LAN, Cellular, or Wireline Lead to Improvements in WLAN? WLAN LAN Cellular Last mile wireline Millions of Bits per second
  • 48. 0 500 1000 1500 2000 0.01 0.1 1 10 100 1000 1880 1900 1920 1940 1960 1980 2000 2020 Did Slowdown in Electricity from Fossil Fuels Lead to Improvements in Nuclear and Solar Cells? Cost of Electricity $/kwH Capital Cost $/kw Fossil Fuels Solar Cells Nuclear Power (capital Cost per kw)
  • 49. 0 5 10 15 20 25 1975 1985 1995 2005 2015 Crystalline Silicon Polysilicon CIGS CdTe Amorphous Silicon Organic Quantum Dots Perovskite Electricity Costs Continued: Did Slowdown in Best Laboratory Efficiency of Crystalline Solar Cells Lead to Improvements in Other Types of Solar Cells
  • 50. Did Slowdown in Old Lighting Lead to Improvements in New Lighting? Source: Lima Azevedo, Granger Morgan, Fritz Morgan, The Transition to Solid-State Lighting, Proceedings of the IEEE 97(3) 0.01 0.1 1 10 100 1850 1875 1900 1925 1950 1975 2000 2025 Fire (Candles, Lamps, Gas Mantles) Incandescent FluorescentLuminosity per Watt
  • 51. 0.01 0.1 1 10 100 1965 1975 1985 1995 2005 Did Slowdown in LEDs (solid circle) Lead to Improvements in OLEDs (empty circle) red LED green LED blue LED white LED green OLEDs yellow OLEDs blue OLEDs white OLEDs LuminosityPerWatt
  • 52. 0.01 0.1 1 10 100 0.1 1 10 100 1000 1980 1985 1990 1995 2000 2005 2010 2015 Did Slowdowns in OLED Luminosity Per Watt (dotted lines) Lead to Improvements in Quantum Dot Efficiencies (solid lines)? Green OLED Yellow OLED Blue OLED Red QD Orange QD Green QD Blue QD Efficiency LuminosityPerWatt
  • 53. Let’s Look at a Statistical Analysis  Are rates of improvement for the old technology slower after the new technology is introduced?  Many ways to do this test  We could introduce a lag, say of 5 years  Let’s just contrast rates of improvement before and after performance metric is recorded for the new technology  Test for change in the slope
  • 54. Technology Domain New Technology Old Tech- nology Rates (%) of Improvement for Old Number of Data Points P- Value Before New After new Before New After New Integrated Circuits Superconducting Josephson Junctions CMOS micro- processor 1976-1990: 19.4 1990-2007: 41 15 17 <.001 Carbon Nano- Tubes for Transistors 1976-1998: 27.1 1998-2007: 34.9 23 9 .00314 Non-Volatile Memory FeRAM NAND Flash memory 1992-2001: 51 2001-2013: 50 9 13 .39 MRAM 1992-2002: 45 2002-2013: 55 10 12 .20 PRAM 1992-2004: 53 2004-2013: 47 12 10 .86 RRAM 1992-2006: 48 2006-2013; 55 14 8 .69 Computing Quantum computers Digital computers 1947-2002: 50 2002-2009: 113 55 13 .524 Rates of Improvement before and after Improvements in New Technology Occur
  • 55. Technology Domain New Tech- nology Old Technology Rates of Improvement (%) for Old Technologies Number of Data Points P- Value Before New After new Before After Telecom WLAN Wireline 1982-1994: 46 1998-2006: 54 4 4 .663 Cellular 1983-1995: 3.4 1995-2012: 87 3 10 <.001 LAN 1979-1995: 33 1995-2004: 78 3 3 .0029 Electricity Crystalline Silicon Solar Fossil Fuel 1882-1957: 5.4 1957-1972: 2.7 14 4 <.001 Nuclear Fossil Fuel 1882-1947: 5.4 1952-1972: 2.6 12 5 <.001 Lighting Fluorescent Incandescent 1950-1970: 0.65 1970-2002: 0.37 10 2 .0012 OLEDs LEDs 1968-1986: 39 1986-2008: 37 5 15 .99 Displays Quantum Dots OLEDs 1987-1996: 35 1996-2011: 22 3 8 .90 Rates of Improvement before and after Improvements in New Technology Occur (continued)
  • 56. Why are New Technologies Being Improved Before Rates have Slowed in Old Technologies?  Many new technologies are simultaneously pursued  Promising ones are pursued more than less promising ones  One reason many new technologies are being pursued  R&D is very decentralized (as mentioned above)  Researchers and (funding agencies) are always looking for something new in order to publish papers  Many researchers have little concern for rate of improvement in old technology
  • 57. Outline  Understanding Supply and Demand Curves  Myths and realities about changes in supply curves  #1: Performance vs. time curves resemble an S-curve  #2: Slowing rate of improvement in old technology drives development of new technology  #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in a technology’s life cycle  #4: Costs fall as cumulative production rises in learning curve  #5: All technologies have the potential for rapid rates of improvements
  • 58. FrequencyofInnovationChange from Product (drives performance improvements) to Process (drives cost reduction) Innovation Product Innovations drive Performance improvements Process innovations drive cost reductions Time Sources: many but first proposed by Utterback and Abernathy
  • 59. RateofInnovationor improvement Change from Product to Process Innovation causes change from Performance Increases to Price/Cost Reductions Product Innovation Process Innovation Time Increases in performance from product innovations Reductions in price from process innovations
  • 60. FrequencyofInnovationandRateofImprovement Myth vs. Reality of Product and Process Innovations Product Innovation Process Innovation Time Increases in performance from product innovations Reductions in price from process innovations FrequencyofInnovationandRateofImprovement Increasing number of inter-related product and and process innovations Time a. The Myth b. The Reality Relatively constant rate of improvements In cost and performance
  • 61. Assuming this Myth is Correct  Performance initially rises while costs stay the same  Later costs fall while performance stays constant  In other words, there should be no correlation between improvements in cost and performance  But the reality is different  Improvements in cost/price and performance are highly correlated  Suggests that improvements are driven by inter-related product and process design changes
  • 62. Technology Dimensions of measure Time Period Data Points Correlation Micro- processor ICs Number of transistors per chip vs. number of transistors/cycle/$ 1976-2011 19 .937 Speed (Hz) vs. number of transistors/Hz/$ 1976-2007 18 .947 Memory (DRAM) ICs Number of transistors per chip vs. price per memory bit 1971-2000 13 .993 Flash Memory Memory capacity vs. bits per price 1992-2008 10 .982 Computers Computations per kwh and computations per unit time and price 1946-2004 31 .920 Magnetic Tape Bits per unit volume and bits per unit cost, 1955-2004 14 .924 Magnetic Disk Bits per unit area and bits per unit cost 1956-2007 11 .967 Trans Atlantic Cable Bits per second and bits per cost per distance 1951-2001 12 .999 DNA Sequencing Speed (bases per person per day) and base pairs per cost 1985-2011 8 .999 Correlation Coefficients Between Performance and Price
  • 63. Technology Dimensions of measure Time Period Data Points Correlation ICE Passenger Car Power per kg and power per cost 1896-1994 55 .931 Power per liter and power per cost 1931-1994 45 .721 Aircraft Power per kg and power per cost 1919-1945 13 .487 Power per liter and power per cost 1919-1945 13 .343 LEDs (Red) Lumen per package and cost per lumen 1973-2005 11 .944 LEDs (White) Lumen per package and cost per lumen 2000-2009 6 .922 GaAs Lasers Power density and 1/price 1987-2007 6 .957 Photo-sensors Resolution ( 1/pixel size) and pixels per price 1987-2007 13 .983 Sensitivity and pixels/price 1987-2007 13 .980 Silicon Solar Cells Efficiency and price per kwH for Silicon 1977-1999 15 .907 Lead Acid Batteries Power per kg and power per cost 1978-1995 12 .99 Power per liter and power per cost 1978-1995 14 .895 Capacitors Power per kg and power per cost 1985-2005 7 .992 High Temperature Superconductor Current x length and current-length per cost 2006-2010 5 .959 Correlation Coefficients Between Performance and Price (continued)
  • 64. 1.00E+06 1.00E+08 1.00E+10 1.00E+08 1.00E+11 1.00E+14 1.00E+17 1970 1980 1990 2000 2010 a. Speed(Hz) andTransistors/Hz/DollarforMicroprocessors 1.00E+08 1.00E+11 1.00E+14 1.00E+17 0.001 0.1 10 1000 1970 1980 1990 2000 2010 b. MillionsofTransistorsandTransistors/Hz/DollarforMicroprocessors 0.00001 0.001 0.1 1 100 10000 1990 1995 2000 2005 2010 d. Numberof MegabitsandGigabitsperDollarfor FlashMemory 1E-09 0.000001 0.001 1 1000 0.0001 0.1 100 100000 100000000 1940 1960 1980 2000 e.MillionsofComputationsperkwHandperDollarfor Computers 100 10000 1000000 100000000 1 100 10000 1000000 1970 1980 1990 2000 2010 Thousands of Bits andBits per Dollarfor DRAM 0.001 0.01 0.1 1 10 100 0.01 0.1 1 10 1990 1995 2000 2005 2010 2015 Areal Recording Density and Gbits per Dollar
  • 65. Improvements in Performance and Cost are Highly Correlated  Suggests theory about increases in performance first occur through changes to product design and later reductions in cost occur through changes in process design is incorrect  One possibility is accidental: firms simultaneously implement innovations in both products and processes in order to reduce costs and improve performance  Second possibility is that product innovations provide improvements in both cost and performance  Third possibility is that the product and process innovations are implemented at the same time because they are inter-related  Evidence for second and third possibilities are presented tonight (see myth #4) and throughout the module  Session 3 will discuss two mechanisms for improvements that involve inter-related product-process innovations
  • 66. Outline  Understanding Supply and Demand Curves  Myths and realities about changes in supply curves  #1: Performance vs. time curves resemble an S-curve  #2: Slowing rate of improvement in old technology drives development of new technology  #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in a technology’s life cycle  #4: Costs fall as cumulative production rises in learning curve  #5: All technologies have the potential for rapid rates of improvements
  • 67. Myth #4: Cumulative Production Drives Cost Reductions  Costs fall as cumulative production grows in learning or experience curve  Learning curve focused on factories making one product  Workers become better at tasks, firms introduce better work flows, (Wright, 1936, Argote and Epple, 1990; Adler and Clark, 1991; Thornton and Thompson, 2001), better process control (Argote, 1999; Lapre et al, 2000), and automated manufacturing equipment (Utterback, 1994), and promote organizational learning (Benkhard, 2000)  Experience curve focuses on impact of cumulative production on R&D  Great R&D leads to improvements in performance  Whichever the case, cumulative production is considered important
  • 68. Myth #4: Cumulative Production Drives Cost Reductions  Implications of learning and experience curves  stimulating demand will lead to cost reductions  This is one reason why many governments subsidize the introduction of clean energy more than they subsidize R&D spending  Clayton Christensen’s theory of disruptive innovation  also implies that increases in demand will lead to reductions in cost and improvements in performance
  • 69. Christensen’s theory of disruptive innovation also implies that performance improvements automatically emerge once a low- end innovation has been found
  • 70. Source of the Myth  Early research noted that costs fall as cumulative production increased for a single design in a single factory (Wright, 1936)  Subsequent research analyzed costs (and later performance) of products that involved new product and process designs and new factories  Analysts did not attempt to disentangle the effects from changes in product and process designs (i.e., laboratory developments) from learning on the factory floor  A few exceptions: some have explicitly questioned learning curves and others implicitly question learning curves by plotting improvements versus time
  • 71. Cumulative Production Costs (log scale) Myth vs. Reality of Cost Reductions (i.e., learning curve) Time Costs (log scale) a. The Myth b. The Reality Start of Commercial Production
  • 72. Reality (1)  Many technologies experience rapid improvements in cost before commercial production begins  Some examples are products/systems in which standard components are experiencing rapid improvements and they have large impact on product cost and performance  Standard components are used in multiple products  For some products, standard components contributes much more to cost than do assembly costs (see next slide)  These standard components make new forms of electronic products or systems economically feasible (see subsequent slide and next session)  These systems/products are becoming economically feasible (even if they have not been introduced) as the cost of components (e.g., ICs) fall and their performance rises  For these types of products, improvements in components have larger impact on product cost than does cumulative product of new product
  • 73. Cost Evolution of iPhone: Assembly costs are low and cost of standard components is high
  • 74. Type of Product Number of Data Points Memory Micro- Proce- ssor Display Came- ra Connec- tivity & Sensors Battery Power Manage- ment Smart Phones 23 15% 22% 22% 8.2% 7.9% 2.3% 3.8% Tablet Compu- ters 33 17% 6.6% 38% 2.9% 6.3% 7.3% 2.5% eBook Readers 9 10% 8.1% 42% 0.30% 8.3% 8.3% NA Game Consoles 2 38% 39% none none NA none 5.8% MP3 Players 9 53% 9% 6% none NA 4% 3.5% TVs 2 7% 4.0% 76% none NA none 3.0% Internet TVs 2 16% 31% none none 10.5% none 3.5% Google Glass 1 17% 18% 3.8% 7.2% 14% 1.5% 4.5% Percentage of Standard Components for Selected Products
  • 75. Reality (2)  Even many “component” or “material-based” technologies experience rapid improvements in cost before commercial production begins  Some improvements can be measured with cost data  Other improvements can be inferred from improvements in performance since improvements in performance often lead to improvements in cost  Improvements in luminosity per watt lead to lower costs on a per lumen basis  Improvements in solar cell efficiency lead to lower costs  Improvements in transistor mobility lead to faster devices and thus lower costs on a per computation basis  These examples are consistent with inter-related product and process innovations that were discussed above in contradicting myth #3
  • 76. Technology Commercial Production Dimensions of measure Time Period Improvement Rate Per Year Organic LEDs 2001 Luminosity per Watt 1987-2005 29% Organic Solar Cells 2010 Efficiency 2001-2013 11.5% Organic Transistors 2007 Mobility 1994-2007 101% Perovskite Cells 2013 Efficiency 2009-2013 >10% Quantum Dot Displays 2013 External Efficiency 1994-2009 79.0% Quantum Dot Solar Cells 2013 Efficiency 2010-2013 42.0% Resistive RAM 2013 Storage Capacity 2006-2013 272% Ferroelectric RAM 2005 2001-2009 37.8% Magnetoresistant RAM 2004 2002-2011 57.8% Phase Change RAM 2006 2004-2012 63.1% Carbon Nanotubes for Transistors 2011 1/Purity 1999-2011 32.1% Density 2006-2011 357% High Temperature Superconductor Wire (YBCO and BSSCO) 2006 Current-length per cost 2004-2010 115% Current x length-BSSCO 1987-2008 32.5% Current x length-YBCO 2002-2011 53.3% Superconducting Josephson Junctions 2011 1/Clock period 1990-2010 20.3% 1/Bit energy 1990-2010 19.8% Qubit Lifetimes 1999-2012 142% Bits/Qubit lifetime 2005-2013 137% Quantum Computers 2011 Number of Qubits 2002-2012 107% Technologies with Rapid Rates of Improvement with little, no Production
  • 77. Technology Start of Commercial Production Recent Sales Data ($ Millions) Sources of Sales Data Organic LEDs 2001 300 in year 2012 (Display Search 2013) Organic Transistors 2007 530 (printed electronics) in Year 2010) (Markets and Markets, 2011) Organic Solar Cells 2010 4.6 in Year 2012 (IDTE, 2012) Quantum Dot Solar Cells 2013 Zero until 2013 (Investorshub, 2013) Quantum Dot Displays 2013 Zero until 2013 (Research & Markets, 2013) Resistive RAM 2013 200 in 2012 (Yole, 2013) Ferroelectric RAM 2005 Magneto-resistant RAM 2004 Phase Change RAM 2006 Single Walled Carbon Nanotubes for Transistors 2011 <10 in 2011 (BCC, 2012) High Temperature Superconductor Wire (YBCO and BSSCO) 2006 30 (2011) 30 (2012) (Connectus, 2012) Superconducting Josephson Junction-based Transistors 2011 First sale in 2011 (Jones, 2013) These Technologies Still don’t have Large Commercial Production
  • 78. 0.1 1 10 100 1000 1985 1990 1995 2000 2005 Green Yellow Blue White Lumens/Watt Why might costs fall as luminosity per Watt rises? start of commercial production Luminosity Per Watt for Organic Light Emitting Diodes
  • 79. 0.000001 0.0001 0.01 1 100 1980 1985 1990 1995 2000 2005 2010 Mobility of Single Crystal and Polycrystalline Organic transistors Single crystal Poly crystalline Mobility(cm2xsec) Start of Commercial Production Why might costs fall as mobility rises?
  • 80. 1998 2002 2006 2010 2014 Organic Quantum Dots Efficiency of Organic and Quantum Dot Solar Cells 25% 5% 0% Efficiency Why might costs fall as efficiency rises? Perovskite solar cells now have about 20% efficiency but without any commercial production
  • 81. 1990 1995 2000 2005 2010 2015 Red Blue Orange Yellow Green Efficiency of Quantum Dot Displays for Different Colors 10% 1% .1% .01% Efficiency Start of Commercial Production Why might costs fall as efficiency rises?
  • 82. 0.001 0.1 10 2001 2003 2005 2007 2009 2011 2013 Phase Change RAM Ferro Electric RAM Magnetic RAM Resistive RAM Number of Memory Bits (Gb) per RAM (Random Access Memory) Chip StorageCapacityperChip(Gb) Why do costs fall as storage capacity rises?
  • 83. 0.01 0.1 1 10 100 0.01 0.1 1 10 100 1995 2000 2005 2010 2015 Density with Inconsistent Feature Size Density with Consistent Feature Size Density(CarbonNanotubes(permicrometer) Purity(%Contaminant) Purity (left axis) and Density (right axis) of Carbon Nano Tubes for Transistors. Density is for Consistent and Inconsistent Feature Size Purity Start of Production
  • 84. 100 1000 10000 100000 2003 2005 2007 2009 2011 $/kilomps-meter Start of Commercial production Cost per kiloamps-meter for Superconducting Cable
  • 85. 1 10 100 1000 1985 1990 1995 2000 2005 2010 2015 YBaCuO BiSr CuO Current (Amps) x Length (km) for Two Types of Superconducting Cables AmpsxLength Start of commercial production Why might costs fall as current x length increases
  • 86. 0.001 0.01 0.1 1 1 10 100 1000 1990 1995 2000 2005 2010 2015 Bit Energy (left axis) and Clock Period (right axis) for Super- conducting Josephson Junctions BitEnergy(FemtoJoules) ClockPeriod(PicoSeconds) Clock Period Bit Energy Start of commercial production Why do costs fall as speeds increase and energy consumption falls?
  • 87. 0.001 0.1 10 1000 100000 1998 2002 2006 2010 2014 Relaxation Time Coherence Time Cavity Lifetime QuBit Lifetime for Several Definitions of "Lifetime" Lifetime(nanoseconds) Start of Commercial Production
  • 88. 1 10 100 1000 10000 2004 2006 2008 2010 2012 2014 The Number of Bits per QuBit Lifetime NumberofBits Start of Commercial Production
  • 89. 1 10 100 1000 2000 2004 2008 2012 NumberofBits Number of Qubits in Quantum Computers (mostly prototypes) Start of Commercial Production
  • 90. Why were these Technologies Improved Before Commercial Production was Started?  Many of the improvements were implemented by university researchers  Primarily motivated by publications and perhaps also patents and forming firms  Others were implemented by startups and corporate labs of large firms (e.g., IBM, Samsung)  Motivated partly by publications and mostly by firm’s desire to commercialize new technologies
  • 91. How were these Technologies Improved? (1)  Creating new materials  Ones with higher luminosity per Watt (both OLEDs and Quantum dots)  Ones that convert more sunlight to electricity (Organic, Quantum Dot, and Perovskite Solar Cells)  Ones with higher mobility (organic transistors)  Ones with higher critical temperatures, magnetic fields, and current densities (superconductors)  New materials also require new processes, so this is consistent with the discussion of myth #3 (inter- related product and process innovations)
  • 92. How were these Technologies Improved? (2)  New processes (really a subset of creating new materials)  Higher purity and density of carbon nano-tubes  Longer Qubit lifetimes and number of Qubits per lifetime  When the material is fixed, the improvements primarily come from changes in processes  Reducing the feature size of memory cells or Josephson Junctions  Non-volatile memory  Superconducting Josephson Junctions  This also requires changes in both product and process design. Smaller feature sizes involve new product designs and they require new processes in order to achieve the smaller feature sizes
  • 93. What Changes when Commercial Production Substantially Increases?  Costs will fall as equipment replaces labor and as larger production equipment is installed (See Session 3)  But changes in product and process design will continue  Increases in production volume will lead to increases in R&D for new product and process designs  Learning occurs on the factory floor for each new product and process design  The dominant mechanism for improvements will depend on the technology  Increases in scale of production equipment  New product and process designs  Larger volumes also  enable purchasing discounts  enables development cost to be amortized over more volumes
  • 94. Implications  Improvements can be achieved without commercial production  These improvements occur in laboratories  If improvements are occurring, the improvements are an important signal concerning the potential of the new technology  If improvements aren’t occurring, maybe the technology doesn’t have ability to be improved  Proponents will argue improvements will soon occur  Accelerations in S-curve  Impact of commercial production  Don’t believe the hype!
  • 95. Outline  Understanding Supply and Demand Curves  Myths and realities about changes in supply curves  #1: Performance vs. time curves resemble an S-curve  #2: Slowing rate of improvement in old technology drives development of new technology  #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in a technology’s life cycle  #4: Costs fall as cumulative production rises in learning curve  #5: All technologies have the potential for rapid rates of improvements
  • 96. Theory Behind Myth: All Technologies have the Potential for Rapid Rates of Improvement  Rates of improvement depend on extent to which firms and governments promote their technologies through  early production  moving down the learning curve  and thus experiencing acceleration in rates of improvements that occur as effects from demand and early production kick in  In some sense, this myth builds from the other four myths.  An acceleration in rate of improvement occurs (Myth #1, S-curves)  as demand and commercial production begin (Myth #3, cost reductions from process improvements and Myth #4, learning in factories)  or as a slowdown in rate of improvement in old technology occurs (Myth #2, slowdowns)  This acceleration causes the new technology to have a rapid rate of improvement and be commercially successful
  • 97. Reality: Most Technologies Experience Slow Rates of Improvement (e.g., <5% per year)  Appliances: residential heat pumps, air conditioners, washing machines, laundry dryers, dishwashers, refrigerators, freezers, light bulbs (Weiss, M., M. Junginger, M. K. Patel and K. Blok 2010. A review of experience curve analyses for energy demand technologies, Technological Forecasting and Social Change 77(3): 411-428)  Materials, beverages, electrical appliances, foods  some chemicals experienced slightly faster rates of improvement, but rarely reached 10% a year  Nagy B, Farmer D, Bui Q, Trancik J 2013. Statistical Basis for Predicting Technological Progress. PLoS ONE 8(2): e52669. doi:10.1371/journal.pone.0052669NREL, 2013
  • 98. Reality (2): Most Technologies Experience Slow Rates of Improvement (e.g., <5% per year)  Look around your homes  Furniture  Appliances  Construction, Education  Automobiles  New water technologies such as reverse osmosis  New energy technologies  Wind Turbines  Batteries for electric vehicles  Many other old and new technologies Note: Accelerations are occurring in some of these technologies, mostly through better information technology
  • 99. Result is that…  Many new technologies don’t have the rates of improvement necessary to  make them economically feasible or  have a large impact on higher-level systems  Their proponents will probably say that  Acceleration in rates of improvement will occur  Costs will fall once we begin production  We should be skeptical  These technologies probably belong on the left side of the next slide
  • 100. Rate of Improvement ExtentofImprovementNeeded Small Large Slow Fast When Will New Technologies Become Economically Feasible? Now or Probably Very Soon Probably Never Within 5 to 15 Years? Within 5-15 Years?
  • 101. Rate of Improvement ExtentofImprovementNeeded Small Large Slow Fast When Will New Technologies Become Economically Feasible? Wind Turbines Electric Vehicles (with same range, price as conventional ones) Many examples One is power ICs for electrification of vehicles Another may be reverse osmosis Most of the technologies shown in subsequent slides. Some require more improvements than do others. Thus some are in top- right and others are in bottom-right quadrant
  • 102. Technologies with Rapid Rates of Improvement  Are more likely to become economically feasible  if they are not used now  Are more likely to become economically feasible for a growing number of applications  if they are already used  Are more likely to have an impact on how we design higher-level systems  Many listed on following pages; implications for  R&D policy  solving global problems such as urban congestion, sustainability
  • 103. Technology Dimensions of measure Time Period Rate Per Year Integrated Circuits Number of transistors per chip 1971-2011 38% Power ICs Current Density 1993-2012 16.1% Camera chips Pixels per dollar 1983-2013 48.7% Light sensitivity 1986-2008 18% MEMS Number of Electrodes per Eye 2002-2013 45.6% Drops per second for printer 1985-2009 61% Organic Transistors Mobility 1994-2007 101% Computers Instructions per unit time 1979-2009 35.9% Instructions per time and dollar 1979-2009 52.2% Technologies Experiencing Rapid Rates of Improvements (Information Transformation)
  • 104. Technology Dimensions of measure Time Period Rate Per Year Carbon Nanotube Transistors 1/Purity (% metallic) 1999-2011 32.1% Density (per micrometer) 2006-2011 357% Superconducting Josephson Junctions 1/Clock period 1990-2010 20.3% 1/Bit energy 1990-2010 19.8% Qubit Lifetimes 1999-2012 142% Bits per Qubit lifetime 2005-2013 137% Photonics Data Capacity per Chip 1983-2011 39.0% Computers Instructions per unit time 1979-2009 35.9% Instructions per time and dollar 1979-2009 52.2% Quantum Computers Number of Qubits 2002-2012 107% Technologies Experiencing Rapid Rates of Improvements (Information Transformation - Continued)
  • 105. Sub-Technology Dimensions of measure Time Period Rate/ Year Magnetic Storage Recording density (disks) 1991-2011 55.7% Recording density (tape) 1993-2011 32.1% Cost per bit 1956-2007 32.7% Flash Memory Storage Capacity 2001-2013 47% Resistive RAM 2006-2013 272% Ferro-electric RAM 2001-2009 37% Phase Change RAM 2004-2012 63% Magneto RAM 2002-2011 58% Technologies Experiencing Rapid Rates of Improvements (Information Storage)
  • 106. Technology Domain Sub-Technology Dimensions of measure Time Period Rate/ Year Information Transmission Last Mile Wireline Bits per second 1982-2010 48.7% Wireless, 100 m Bits per second 1996-2013 79.1% Wireless, 10 m 1995-2010 58.4% Wireless, 1 meter (USB) 1996-2008 77.8% Materials Transformation Carbon Nanotubes 1/Minimum Theoretical Energy for Production 1999-2008 86.3% Biological Trans- formation DNA Sequencing per unit cost 2001-2013 146% Synthesizing per cost 2002-2010 84.3% Cellulosic Ethanol Output per cost 2001-2012 13.9% Technologies Experiencing Rapid Rates of Improvements
  • 107. Technologies Experiencing Rapid Rates of Improvements Technology Domain Sub- Technology Dimensions of measure Time Period Rate Per Year Energy Trans- formation Light Emitting Diodes (LEDs) Luminosity per Watt 1965-2008 31% Lumens per Dollar 2000-2010 40.5% Organic LEDs Luminosity per Watt 1987-2005 29% GaAs Lasers Power/length-bar 1987-2007 30% LCDs Square meters/dollar 2001-2011 11.0% Quantum Dot Displays External Efficiency 1994-2009 79.0% Solar Cells Peak Watt Per Dollar 2004-2013 21.0% Photo-sensors (Camera chips) Pixels per dollar 1983-2013 48.7% Light sensitivity 1986-2008 18% Energy Transmission Super- conductors Current-length/dollar 2004-2010 115% Current x length-BSSCO 1987-2008 32.5% Current x length-YBCO 2002-2011 53.3%
  • 108. Outline  Science, technology, and innovation  Supply and demand curves for new technologies  Myths and realities about technology change  #1: Performance vs. time curves resemble an S-curve  #2: Slowing rate of improvement in old technology drives development of new technology  #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  #4: Costs fall as cumulative production rises in learning curve  #5: All technologies have the potential for rapid rates of improvements  Pulling these myths and realities together
  • 109. The Reality (1)  Technologies experience rather constant rates of improvement, although  accelerations and slowdowns do occur and sometimes occur in the middle of a technology’s life cycle  Limits also exist but are often much further away than ordinarily thought  Rather constant rates of improvement enable us to predict to some extent future rates of improvement  These rather constant rates of improvement also help us think of and analyze new systems that are constructed from these better components  We want to understand what types of new technologies or systems will likely emerge in the near future
  • 110. The Reality (2)  Most of the improvements are made in development labs and involve inter-related product-process innovations  Some of these improvements occur before commercial production occurs, some after production starts  We should be suspicious of proponents arguing that costs will fall as we make more of them for at least two reasons  small impact of learning by doing in factories  many improvements (both in product and process design) occur before start of commercial production  some exceptions (products that greatly benefit from increases in scale of production equipment)  Since improvements are often made before commercial production starts, we can use rates of improvements to partly predict future rates of improvement
  • 111. The Reality (3)  We will cover many technologies in this module, most of which experience rapid rates of improvement  But we will not cover all of them, and some of you will find other technologies with rapid rates of improvement for your group projects  Understanding how the improvements occur can also help us to better understand the future  Can help us understand whether rates of improvement will continue  Can also help us predict rates of improvement if no improvements have yet occurred  How improvements occur will be covered in the next session
  • 112. For your Presentation  Choose a technology that has or you think has or will have a rapid rate of improvement  Think about applications for the technology and how this technology might change the world

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