Five myths of technology change
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Five myths of technology change

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these slides challenge five myths of 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: ...

these slides challenge five myths of 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: Rates of improvements are similar for most technologies.

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Five myths of technology change Five myths of technology change Presentation Transcript

  • 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
  • Outline  Myth #1: Performance vs. time curves resemble an S-curve  Myth #2: Slowing rate of improvement in old technology drives development of new technology  Myth #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  Myth #4: Costs fall as cumulative production rises in learning curve  Myth #5: All technologies have the potential for rapid rates of improvements  Pulling these myths and realities together
  • Time Performance Myth: New Technologies Follow S-Curves Emergence of New Technology
  • 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)
  • S-Curves make it easy to fall for hype
  • Let’s look at some real data Mostly straight lines on a logarithmic plot but with some deviations
  • 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?
  • 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?
  • 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?
  • 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?
  • 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
  • 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)  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
  • 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  We will show this data later this semester.  Not enough data points, but straight line
  • 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
  • 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? Let’s look at the statistical analysis  Linear model  Logarithmic model  Logarithmic model with time squared
  • 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
  • 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)
  • 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
  • Outline  Myth #1: Performance vs. time curves resemble an S-curve  Myth #2: Slowing rate of improvement in old technology drives development of new technology  Myth #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  Myth #4: Costs fall as cumulative production rises in learning curve  Myth #5: All technologies have the potential for rapid rates of improvements  Pulling these myths and realities together
  • 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
  • Technology Domain Technology Dimension of Measure Time Period Improvemen Rate Per Ye 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% 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% Efficiencies 1957-2001 2.2% Other Solar, many 1975-2013 5-50% 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% Micro- processor CMOS 1/Clock Period 1986-2012 24% Super-conducting Josephson Junction 1990-2010 20.3% Computing Digital Instructions per unit time 1947-2009 50% Quantum Number of Qubits 2002-2012 107% Telecom Trans-Atlantic Crossing Bits per second 1951-2001 36% Last Mile Wireline 1982-2010 48.7% Cellular 1996-2013 79.1% LAN 1995-2010 58.4% WLAN 1996-2008 77.8% Rates of Improvements for Technologies that Might be Considered Substitutes
  • 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
  • 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
  • 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)
  • 1 100 10000 1000000 1975 1980 1985 1990 1995 2000 2005 2010 Did Slowdown in CMOS Microprocessors Lead to Improvements in Superconducting Josephson Junctions? CMOS Microprocessors Superconducting Josephson Junctions Clock Period (pico-
  • Did Slowdown in Fluorescent Lighting Lead to Improvements in LEDs/SSL? (SSL = Solid State Lighting) Source: Lima Azevedo, Granger Morgan, Fritz Morgan, The Transition to Solid-State Lighting, Proceedings of the IEEE 97(3) LuminosityperWatt
  • 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
  • 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
  • 0.01 0.1 1 10 100 1000 1880 1900 1920 1940 1960 1980 2000 2020 Did Slowdown in Conventional Electricity Generation (Fossil Fuels) Lead to Improvements in Solar Cells? Conventional Solar $/kwH
  • 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
  • 0 5 10 15 20 25 1975 1985 1995 2005 2015 Did Slowdown in Best Laboratory Efficiency of Crystalline Solar Cells Lead to Improvements in Other Types of Solar Cells Crystalline Silicon Polysilicon CIGS CdTe Amorphous Silicon Organic Quantum Dots Perovskite
  • 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
  • Technology Domain New Technology Old Tech- nology Rates of Improvement for Old Technology Number of Data Points P- Value Before New After new Before After Lighting LEDs Fluor- escent 1950-1970: 0.65% 1970-2002: 0.37% 10 2 .0012 OLEDs LEDs 1968-1986: 38.7% 1986-2008: 37.0% 5 15 .99 Displays Quantum Dots OLEDs 1987-1996: 35% 1996-2011: 22% 3 8 .90 Electricity Crystalline Silicon Solar Fossil Fuel 1882-1957: 5.4% 1957-1972: 2.7% 14 4 <.001 Nuclear 1882-1947: 5.4% 1952-1972: 2.6% 12 5 <.001 Non-Volatile Memory MRAM (and others) Flash memory 1992-2001: 51% 2001-2013: 50% 9 13 .39 Micro- processor Josephson Junction CMOS 1976-1990: 19.4 1990-2012: 41% 15 17 <.001 Computing Quantum computers Digital computer 1947-2002: 50% 2002-2009: 113% 55 13 .524 Telecom WLAN Wireline 1982-1994: 46% 1998-2006: 54% 4 4 .663 Cellular 1983-1995: 3.4% 1995-2012: 87% 3 10 <.001 Rates of Improvement before and after Improvements in New Technology Occur
  • Why are New Technologies Being Improved Before Rates have Slowed in Old Technologies?  R&D is very decentralized (as mentioned above)  Researchers and (funding agencies) are always looking for something new in order to publish papers  Many have little concern for rate of improvement in old technology  The result is that many new technologies are simultaneously pursued  Promising ones are pursued more than less promising ones  Political and organizational factors also impact on R&D funding
  • Outline  Myth #1: Performance vs. time curves resemble an S-curve  Myth #2: Slowing rate of improvement in old technology drives development of new technology  Myth #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  Myth #4: Costs fall as cumulative production rises in learning curve  Myth #5: All technologies have the potential for rapid rates of improvements  Pulling these myths and realities together
  • 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
  • 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
  • Assuming the Previous Slide is True  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
  • 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
  • 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)
  • 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
  • Improvements in Performance and Cost are Highly Correlated  One possibility is product design changes do not precede those of process design changes and that firms are simultaneously implementing innovations in both products and processes throughout a technology’s life cycle.  Alternatively, they might be implementing product and process innovations at the same time because the innovations are inter-related  Evidence for this will be presented throughout the module  Session 3 will discuss two mechanisms for improvements that involve inter-related product-process innovations
  • Outline  Myth #1: Performance vs. time curves resemble an S-curve  Myth #2: Slowing rate of improvement in old technology drives development of new technology  Myth #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  Myth #4: Costs fall as cumulative production rises in learning curve  Myth #5: All technologies have the potential for rapid rates of improvements  Pulling these myths and realities together
  • Myth: Cumulative Production Drives Cost Reductions  Costs fall as cumulative production grows in learning or experience curve  better process control (Clark and Fujimoto, 1990; Argote, 1999; Lapre et al, 2000) and inventory management  better automated manufacturing equipment (Utterback, 1994)  organizational learning (Benkhard, 2000)  Implications: 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
  • Reality (1)  Many technologies experience rapid improvements in cost before commercial production begins  Most obvious examples are systems in which components are experiencing rapid improvements  New forms of electronic products or systems (see next slide)  These systems are becoming economically feasible as the cost of components (e.g,, ICs) fall and their performance rises  These systems don’t experience commercial production until they are economically feasible
  • Laptops MP3 Players Video Set-top boxes E-Book Readers Games Web Browsers Digital TV Mobile Digital Cameras Smart Phones Phones PDAs Tablet Computers Increases in the Number of Transistors Make New Forms of Electronic Products Economically Feasible Calculators Digital Watches PCs
  • 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
  • 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
  • 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 (Yole, 2013) Ferroelectric RAM 2005 Magneto-resistant RAM 2004 Phase Change RAM 2006 Single Walled Carbon Nanotubes for Transistors 2011 <10 (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) Quantum Computers 2011
  • 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
  • 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?
  • 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 17.9% efficiency but without any commercial production
  • 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?
  • 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?
  • 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
  • 100 1000 10000 100000 2003 2005 2007 2009 2011 $/kilomps-meter Start of Commercial production Cost per kiloamps-meter for Superconducting Cable
  • 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
  • 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
  • 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
  • 1 10 100 1000 10000 2004 2006 2008 2010 2012 2014 The Number of Bits per QuBit Lifetime NumberofBits Start of Commercial Production
  • 1 10 100 1000 2000 2004 2008 2012 NumberofBits Number of Qubits in Quantum Computers (mostly prototypes) Start of Commercial Production
  • 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
  • How were these Technologies Improved?  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 required new processes  Reducing the feature size of memory cells or Josephson Junctions  Non-volatile memory  Superconducting Josephson Junctions  New processes  Higher purity and density of carbon nano-tubes  Longer Qubit lifetimes and number of Qubits per lifetime
  • However……………  Cumulative production does impact on costs  Higher production volumes enables larger production equipment, which is relevant for some technologies  But not all, what about the iPad?  Discussed more in Session 3  Larger volumes  enable purchasing discounts  enables development cost to be amortized over more volumes  makes it easier to justify higher development budgets
  • Implications  We can achieve improvements without commercial production  These improvements are done in laboratories  If the technology is in the lab, improvements should be occurring  If they 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!
  • Outline  Myth #1: Performance vs. time curves resemble an S-curve  Myth #2: Slowing rate of improvement in old technology drives development of new technology  Myth #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  Myth #4: Costs fall as cumulative production rises in learning curve  Myth #5: All technologies have the potential for rapid rates of improvements  Pulling these myths and realities together
  • 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.
  • Reality: Most Technologies Experience Slow Rates of Improvement (e.g., <5% per year)  For example, one study found that most technologies have rates of improvement less than 5% a year  Materials  Beverages  Foods  Electrical Appliances  Some chemicals experienced rates higher than 5% but not more than 10% per 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.
  • 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
  • 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
  • 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?
  • 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 is 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
  • 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
  • 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)
  • 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 Number of Optical Channels 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)
  • 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)
  • 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
  • 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%
  • Outline  Myth #1: Performance vs. time curves resemble an S-curve  Myth #2: Slowing rate of improvement in old technology drives development of new technology  Myth #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle  Myth #4: Costs fall as cumulative production rises in learning curve  Myth #5: All technologies have the potential for rapid rates of improvements  Pulling these myths and realities together
  • The Reality (1)  Technologies experience rather constant rates of improvement, although  accelerations and slowdowns do 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
  • 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 process innovations  many improvements 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
  • 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, so one challenge for you is to find other technologies with rapid rates of improvement  Understanding the drivers of the improvements 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  The drivers of improvements will be covered in the next session
  • 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
  • For more information  http://www.slideshare.net/Funk98/presentations  eBook by Jeffrey Funk and Christopher Magee, Exponential Change: What drives it? What does it tell us about the future? http://www.amazon.com/Exponential-Change-drives- about-future-book/dp/B00HPSAYEM/ref=sr_1_1?s=digital- text&ie=UTF8&qid=1399871060&sr=1- 1&keywords=exponential+change