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Intervention de José Halloy (Professeur de Physique à l'Université Paris Diderot), dans le Cadre du Café-In du jeudi 11 mai 2017, dans les locaux du centre de recherche d'Inria Sophia Antipolis - Méditerranée.

Researcher (Origens Medialab, ESC Clermont). Web Architect. Head of the Master of Science "Strategy and Design for the Anthropocene"

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- 1. La question de la durabilité des technologies de calcul et de télécommunication José Halloy Prof. of Physics, Université Paris Diderot
- 2. Sustainability: long duration Energy and resources transition Climate change
- 3. It’s not climate change – it’s everything change Margaret Atwood
- 4. AlphaGo defeated Lee Sedol ARTICLE doi:10.1038/nature16961 Mastering the game of Go with deep neural networks and tree search David Silver1 *, Aja Huang1 *, Chris J. Maddison1 , Arthur Guez1 , Laurent Sifre1 , George van den Driessche1 , Julian Schrittwieser1 , Ioannis Antonoglou1 , Veda Panneershelvam1 , Marc Lanctot1 , Sander Dieleman1 , Dominik Grewe1 , John Nham2 , Nal Kalchbrenner1 , Ilya Sutskever2 , Timothy Lillicrap1 , Madeleine Leach1 , Koray Kavukcuoglu1 , Thore Graepel1 & Demis Hassabis1 All games of perfect information have an optimal value function, v* (s), which determines the outcome of the game, from every board position or state s, under perfect play by all players. These games may be solved by recursively computing the optimal value function in a search tree containing approximately bd possible sequences of moves, where b is the game’s breadth (number of legal moves per position) and d is its depth (game length). In large games, such as chess (b≈35, d≈80)1 and especially Go (b≈250, d≈150)1 , exhaustive search is infeasible2,3 , but the effective search space can be reduced by two general principles. First, the depth of the search may be reduced by position evaluation: truncating the search tree at state s and replacing the subtree below s by an approximate value function v(s)≈v* (s) that predicts the outcome from state s. This approach has led to superhuman performance in chess4 , checkers5 and othello6 , but it was believed to be intractable in Go due to the complexity of the game7 . Second, the breadth of the search may be reduced by sampling actions from a policy p(a|s) that is a prob- ability distribution over possible moves a in position s. For example, Monte Carlo rollouts8 search to maximum depth without branching at all, by sampling long sequences of actions for both players from a policy p. Averaging over such rollouts can provide an effective position evaluation, achieving superhuman performance in backgammon8 and Scrabble9 , and weak amateur level play in Go10 . Monte Carlo tree search (MCTS)11,12 uses Monte Carlo rollouts to estimate the value of each state in a search tree. As more simu- policies13–15 or value functions16 based on a linear combination of input features. Recently, deep convolutional neural networks have achieved unprec- edented performance in visual domains: for example, image classifica- tion17 , face recognition18 , and playing Atari games19 . They use many layers of neurons, each arranged in overlapping tiles, to construct increasingly abstract, localized representations of an image20 . We employ a similar architecture for the game of Go. We pass in the board position as a 19×19 image and use convolutional layers to construct a representation of the position. We use these neural networks to reduce the effective depth and breadth of the search tree: evaluating positions using a value network, and sampling actions using a policy network. We train the neural networks using a pipeline consisting of several stages of machine learning (Fig. 1). We begin by training a supervised learning (SL) policy network pσ directly from expert human moves. This provides fast, efficient learning updates with immediate feedback and high-quality gradients. Similar to prior work13,15 , we also train a fast policy pπ that can rapidly sample actions during rollouts. Next, we train a reinforcement learning (RL) policy network pρ that improves the SL policy network by optimizing the final outcome of games of self- play. This adjusts the policy towards the correct goal of winning games, rather than maximizing predictive accuracy. Finally, we train a value network vθ that predicts the winner of games played by the RL policy network against itself. Our program AlphaGo efficiently combines the The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state- of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away. 4 8 4 | N A T U R E | V O L 5 2 9 | 2 8 J A N U A R Y 2 0 1 6 1 Google DeepMind, 5 New Street Square, London EC4A 3TW, UK. 2 Google, 1600 Amphitheatre Parkway, M *These authors contributed equally to this work. © 2016 Macmillan Publishers Limited ARTICLERESEARCH Figure 3 | Monte Carlo tree search in AlphaGo. a, Each simulation is evaluated in two ways: using the value network vθ; and by running Selectiona b c dExpansion Evaluation Backup p p Q + u(P) Q + u(P)Q + u(P) Q + u(P) P P P P Q Q QQ Q rr r r P max max P ARTICLE RESEARCH sampled state-action pairs (s, a), using stochastic gradient ascent to maximize the likelihood of the human move a selected in state s ∆σ σ ∝ ∂ ( | ) ∂ σp a slog We trained a 13-layer policy network, which we call the SL policy network, from 30 million positions from the KGS Go Server. The net- and its weights ρ are initialized to the same values, ρ=σ. We play games between the current policy network pρ and a randomly selected previous iteration of the policy network. Randomizing from a pool of opponents in this way stabilizes training by preventing overfitting to the current policy. We use a reward function r(s) that is zero for all non-terminal time steps t<T. The outcome zt =±r(sT) is the termi- nal reward at the end of the game from the perspective of the current player at time step t: +1 for winning and −1 for losing. Weights are Figure 1 | Neural network training pipeline and architecture. a, A fast rollout policy pπ and supervised learning (SL) policy network pσ are trained to predict human expert moves in a data set of positions. A reinforcement learning (RL) policy network pρ is initialized to the SL policy network, and is then improved by policy gradient learning to maximize the outcome (that is, winning more games) against previous versions of the policy network. A new data set is generated by playing games of self-play with the RL policy network. Finally, a value network vθ s trained by regression to predict the expected outcome (that is, whether the current player wins) in positions from the self-play data set. b, Schematic representation of the neural network architecture used in AlphaGo. The policy network takes a representation of the board position s as its input, passes it through many convolutional layers with parameters σ (SL policy network) or ρ (RL policy network), and outputs a probability distribution ( | )σp a s or ( | )ρp a s over legal moves a, represented by a probability map over the board. The value network similarly uses many convolutional layers with parameters θ, but outputs a scalar value vθ(s′) that predicts the expected outcome in position s′. Regression Classification Classification SelfPlay Policy gradient a b Human expert positions Self-play positions NeuralnetworkData Rollout policy p p p (a⎪s) (s′)p SL policy network RL policy network Value network Policy network Value network s s′ sampled state-action pairs (s, a), using stochastic gradient maximize the likelihood of the human move a selected in st ∆σ σ ∝ ∂ ( | ) ∂ σp a slog We trained a 13-layer policy network, which we call the network, from 30 million positions from the KGS Go Server work predicted expert moves on a held out test set with an ac 57.0% using all input features, and 55.7% using only raw bo Figure 1 | Neural network training pipeline and architecture. a rollout policy pπ and supervised learning (SL) policy network pσ trained to predict human expert moves in a data set of positions. A reinforcement learning (RL) policy network pρ is initialized to policy network, and is then improved by policy gradient learning maximize the outcome (that is, winning more games) against pre versions of the policy network. A new data set is generated by pla games of self-play with the RL policy network. Finally, a value ne is trained by regression to predict the expected outcome (that is, Classification Classification SelfP Policy gradient Human expert positions Se ARTICLERESEARCH Figure 3 | Monte Carlo tree search in AlphaGo. a, Each simulation traverses the tree by selecting the edge with maximum action value Q, is evaluated in two ways: using the va a rollout to the end of the game with t Selectiona b c dExpansion Evaluation p p Q + u(P) Q + u(P)Q + u(P) Q + u(P) P P P P Q rr P max max P
- 5. AlphaGo defeated Lee Sedol ~100 W per CPU ~200 W per GPU
- 6. AlphaGo defeated Lee Sedol: energy cost Brain ~ 20 W 2500 kCal/day ~ 120 W 130 GJ = 9.7 days 34 years = 130 GJ ~ 155 kW
- 7. Current computing industry is based on crystalline semiconductors
- 8. power than those made with vacuum and diodes, and the transition to tra also led to a period of great techn innovation as engineers experiment different ways to build these m to maximize performance and i reliability. Computations per kilowatt-hour d every 1.57 years over the entire ana riod, a rate of improvement only slower than that for PCs, which s ciency double every 1.52 years fro to 2009 (see Figure 4). The data show icant increases in computational ef even during the vacuum tube and d transistor eras. From 1946 (ENIAC) (when the last of the primarily tub computers in our sample came o computations per kilowatt-hour d every 1.35 years. Computations per k hour increased even more rapidly du shift from tubes to transistors, but t of change slowed during the era of transistors. In the recent years for which w more than a few data points (2001 2008, and 2009), there is a factor of three separating the lowest and high mates of computations per kilowa which indicates substantial variation data in any given year. This variation the result of including different t Figure 2. Computational capacity over time (computations/second per computer). These data are based on William D. Nordhaus’ 2007 work,9 with additional data added post-1985 for computers not considered in his study. Doubling time for personal computers only (1975 to 2009) is 1.5 years. Koomey, Jonathan, et al. "Implications of historical trends in the electrical efficiency of computing." IEEE Annals of the History of Computing 33.3 (2011): 46-54.
- 9. Koomey, Jonathan G., H. Scott Matthews, and Eric Williams. "Smart everything: Will intelligent systems reduce resource use?." Annual Reviewof Environment and Resources 38 (2013): 311- 343.1E+00 1E+01 1E+02 1E+03 1E+04 1E+05 1E+06 1E+07 1E+08 1E+09 1E+10 1E+11 1E+12 1E+13 1E+14 1E+15 1E+16 1940 1950 1960 1970 1980 1990 2000 2010 ComputationsperkWh 2008+2009 laptops Cray 1 supercomputer Univac III (transistors) Eniac IBM PC-XT Univac II Univac I EDVAC IBM PC-AT 486/25 and 486/33 Desktops SiCortex SC5832 SDS 920 DEC PDP-11/20 IBM PC Gateway P3, 733 MHz IBM PS/2 E + Sun SS1000 Altair 8800 Apple IIe Macintosh 128k Compaq Deskpro 386/20e Figure 2 Annu.Rev.Environ.Resour.2013.38:311-343.Downloadedfromwww.annualreviews.org Accessprovidedby81.194.30.216on12/14/16.Forpersonaluseonly.
- 10. Fig. 1 Schematic flow diagram of integrated-circuit fabrication. Fig. 2 Size comparison of a wafer to individual components. (a) Semiconductor wafer. (b) Chip. (c) MOSFET and bipolar transistor.
- 11. Table 2.6. Part of the Periodic Table of elements showing a selection of dopant and alloy elements for silicon. Lower indices show the atomic mass number and upper indices the atomic number of each element Main Group III IV V B5 10.8 C6 12.0 N7 14.0 Al13 27.0 Si14 28.1 P15 31.0 Ga31 69.7 Ge32 72.6 As33 74.9 In49 114.8 Sn50 118.7 Sb51 121.7 shell electrons), the chemical behaviour, the solid solubility and the diﬀusion constant. The atomic radius of nitrogen is too small for a substitutional position in Si, it therefore fails as a donor in Si. Boron and phosphorus are smaller than Si (covalent radius of Si: 0.117 nm), therefore thick and heavily doped layers are under tensile strain. Arsenic has a near identical covalent radius to Si, therefore heavily n-doped layers (e.g. buried layers for collector contacts) are preferably As doped. Antimony (as Ga, In) is larger than Si, which results in compressively strained layers. The maximum solid solubility is obtained 100 ◦ C to 150 ◦ C below the melting point. It ranges from about 1021 cm−3 for B and As to several 1018 cm−3 for Ga. At the low temperatures used for the fabrication of many quantum eﬀect devices the solid solubilities are considerably lower, but metastable high solubilities can be obtained easily. From the four main dopants B, P, As, Sb the former two (B, P) diﬀuse faster than the later ones (As, Sb). At lower process temperatures (< 850 ◦ C) transient enhanced diﬀusion (TED) created by nonequilibrium point defect 60 3. Resum´e of Semiconductor Physics Ge Sia a Diamond Zinc Blend Fig. 3.4. (a) The diamond crystal structure of elemental Si and Ge. The zinc blend structure is that found for compound semiconductors such as GaAs and ordered Si0.5Ge0.5 Γ L X K W Σ ∆ Λ U Fig. 3.5. The body centred cubic reciprocal lattice along with the Brillouin zone boundaries and the major symmetry points marked on the using the standard group theory symbols The unit cell of the reciprocal lattice can be formed by constructing the Wigner-Seitz cell. The Wigner-Seitz cell is primitive and displays the sym- metry of the crystal system. To obtain this cell one must start at any of the lattice points and the origin and draw vectors to all the neighbouring lattice points. Planes perpendicular to and passing through the midpoints of these vectors are constructed. The Wigner-Seitz cell is then the smallest volume about the origin bounded by these planes. For a face centred cubic lattice, the reciprocal lattice which forms the 102 101 100 10-1 10-2 10-3 10-4 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 Impurity concentration (cm-3 ) p-type (B) n-type (P) Resistivity(W-c Fig. 3.18. The resistivity of n and p-doped Si as a function of doping density (after Sze) Si Si Si Si SiSiSi SiSi Si Si Si Si SiSiSi P + Si Si Si Si Si SiSiSi B - Si q + q - Fig. 3.19. Electrical conduction in semiconductors using the s−p3 bonding picture. (a) Intrinsic silicon has no free electrons and is an insulator. (b) P doped material has a free electron for each P atom and hence is n-type. (c) B doped material has an electron missing from a bond and hence is p-type where kB is the Boltzmann constant, T is the absolute temperature and EF is the Fermi energy. The Fermi energy, EF is deﬁned as the energy which sep- "Silicon Quantum Integrated Circuits. Silicon-Germanium Heterostructure Devices: Basics and Realisations” E. Kasper and D. J. Paul Springer, Berlin, 2005
- 12. 10.7 Strained-Si CMOS p-Si substrate p-Si1-yGey graded buffer p-Si1-yGey buffer p-Si1-yGey well n-Si1-yGey well STI STI STI n+ poly p+ poly silicide silicide silicide silicidesilicidesilicide p strained-Si n strained-Si oxide n+ n+ p+ p+ oxide p-Si1-yGey punch stop n-Si1-yGey punch stop n-MOS p-MOS Fig. 10.23. A schematic diagram of strained-Si n- and p-MOS transistors. The Ge content in the substrate (y) can be chosen to optimise the performance of either the n- or p-MOS transistor or both
- 13. Fig. 19 Monolithic tandem solar cell.1 It has a higher built-in voltage and hence a higher open-circuit voltage, and a higher cell efficiency. High Photodetectors and Solar Cells 345 Same issues for photovoltaics crystalline semiconductors
- 14. 1E+00 1E+01 1E+02 1E+03 1E+04 1E+05 1E+06 1E+07 1E+08 1E+09 1E+10 1E+11 1E+12 1E+13 1E+14 1E+15 1E+16 1E+17 1E+18 1E+19 1E+20 1E+21 1E+22 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 ComputationsperkWh 2008+2009 laptops Univac III (transistors) Eniac IBM PC Apple IIe Feynman's limit (3 atom transistor) Figure 3 Annu.Rev.Environ.Resour.2013.38:311-343.Downloadedfromwww.annualreviews.org Accessprovidedby81.194.30.216on12/14/16.Forpersonaluseonly. Reaching the physical limits
- 15. Figure 2.1:- An energy - delay diagram for single electronics. Room temperature operation can only be achieved to the right of the line labelled room temperature. The lower left-hand corner of the diagram is inaccessible due to quantum fluctuations and the lower right-hand corner of the diagram is inaccessible due to dissipation. The dissipation limit is represented by three lines, each corresponding to a different device density, n. The current trends in CMOS and single electronics are indicated in the diagram [Hadley 1996]. Energy dissipation as heat is an important issue
- 16. Energy dissipation as heat is an important issue
- 17. Criticality of metals and metalloids T. E. Graedela,b,1 , E. M. Harpera , N. T. Nassara , Philip Nussa , and Barbara K. Recka a Center for Industrial Ecology, Yale University, New Haven, CT 06511; and b Stellenbosch Institute for Advanced Study, Stellenbosch 7602, South Africa Edited by B. L. Turner, Arizona State University, Tempe, AZ, and approved February 27, 2015 (received for review January 8, 2015) Imbalances between metal supply and demand, real or antici- pated, have inspired the concept of metal criticality. We here characterize the criticality of 62 metals and metalloids in a 3D “criticality space” consisting of supply risk, environmental implica- tions, and vulnerability to supply restriction. Contributing factors that lead to extreme values include high geopolitical concentra- tion of primary production, lack of available suitable substitutes, and political instability. The results show that the limitations for many metals important in emerging electronics (e.g., gallium and selenium) are largely those related to supply risk; those of plati- num group metals, gold, and mercury, to environmental implica- tions; and steel alloying elements (e.g., chromium and niobium) as well as elements used in high-temperature alloys (e.g., tungsten and molybdenum), to vulnerability to supply restriction. The met- als of most concern tend to be those available largely or entirely as byproducts, used in small quantities for highly specialized applica- tions, and possessing no effective substitutes. economic geology | materials science | substitution | supply risk | sustainability Modern technology relies on virtually all of the stable ele- ments of the periodic table. Fig. 1, which pictures the concentrations of elements on a printed circuit board, provides an illustration of that fact. The concentrations of copper and iron are obviously the highest, and others such as cesium are much lower, but concentration clearly does not reflect elemental im- portance: all of the elements are required to maintain the func- tions for which the board was designed. However, some elements may not be routinely available well into the future. How is this risk Despite one’s intuition that it should be straightforward to designate one element as critical and another as not, determining criticality turns out to be very challenging indeed. This is because criticality depends not only on geological abundance, but on a host of other factors such as the potential for substitution, the degree to which ore deposits are geopolitically concentrated, the state of mining technology, the amount of regulatory oversight, geopolitical initiatives, governmental instability, and economic policy (10). As various organizations (e.g., refs. 11–13) have attempted to determine resource criticality in recent years, a va- riety of metrics and methodological approaches have been cho- sen. The predictable result has been that criticality designations have differed widely (14), thus offering relatively little guidance to industrial users of the resources or to governments concerned about the resilience of their supplies. In an effort to bring enhanced rigor and transparency to the evaluation of resource criticality, we have developed a quite comprehensive methodology. It is applicable to users of different organizational types (e.g., corporations, national governments, global-level analysts) and is purposely flexible so as to allow user control over aspects of the methodology such as the relative weighting of variables. As with any evaluation using an aggre- gation of indicators, the choice of those indicators is, in part, an exercise in judgment (15), but alternative choices have been evaluated over several years and we believe all of our final choices to be defendable in detail. We have applied the methodology to 62 metals and metal- loids (hereafter termed “metals” for simplicity of exposition)— essentially all elements except highly soluble alkalis and halo- gens, the noble gases, nature’s “grand nutrients” (carbon, nitrogen, S Direct Submission. ndence should be addressed. Email: thomas.graedel@yale.edu. supporting information online at www.pnas.org/lookup/suppl/doi:10. 12/-/DCSupplemental. PNAS | April 7, 2015 | vol. 112 | no. 14 | 4257–4262 SUSTAINA SCIEN There are only a few metals that have an overall high score along the supply risk dimension (i.e., the metals that have small geological resources relative to their current demands and that are mainly recovered as byproducts of other metals, with byproducts called companions in our analysis). These include indium, arsenic, thallium, antimony, silver, and selenium, metals important in Fig. 6. Periodic tables of criticality for 62 metals, 2008 epoch, global level for (A) supply risk, (B) environmental implications, and (C) vulnerability to supply restriction.
- 18. REVIEW Challenges in Metal Recycling Barbara K. Reck* and T. E. Graedel Metals are infinitely recyclable in principle, but in practice, recycling is often inefficient or essentially nonexistent because of limits imposed by social behavior, product design, recycling technologies, and the thermodynamics of separation. We review these topics, distinguishing among common, specialty, and precious metals. The most beneficial actions that could improve recycling rates are increased collection rates of discarded products, improved design for recycling, and the enhanced deployment of modern recycling methodology. As a global society, we are currently far away from a closed-loop material system. Much improvement is possible, but limitations of many kinds—not all of them technological—will preclude complete closure of the materials cycle. T he generation now between the ages of 20 and 30 is, in many parts of the world, the first to have grown up with the re- cycling bin as a normal part of life. Discarded paper, cans, and bottles have designated places to go, and often go there. The situation is less cer- tain for products used for a number of years be- fore being discarded—computers, refrigerators, automobiles—for which recycling procedures have been diverse and sporadic. And few know what happens to obsolete equipment used on behalf of individuals but owned by corporations or organizations—medical imaging machines, air- craft engines, and the like. The recycling of products in the “occasional- ly discarded” or “owned by somebody else” cat- egories is complicated by the rapid expansion of the designer’s materials palette that has taken place in the past several decades (1, 2). Today, virtually every stable element in the periodic ta- ble is used so as to take advantage of its unique physical and chemical properties. The result is that many products are more functional and reli- able than before. An unintended consequence is that recycling has become much more compli- cated and challenging. Several reviews of metal recycling have ap- peared in recent years (3–5). They discuss central issues such as recycling technologies, economic limitations, and methods of enhancement. Some open questions still remain: How much is going on, and what are the trends? What are its limits? Is a closed-loop materials economy possible? It is these systems-level topics that are the focus of the present work. The Current Status of Metal Recycling How well is the world doing at recycling the di- verse mix of elements in modern products? Two metrics answer this question best: recycled con- tent and end-of-life recycling rate (EOL-RR). Re- cycled content describes the share of scrap in metal production, which is important to get a sense of the magnitude of secondary supply. This indicator, however, has two limitations. First, life- times of metal-containing products often span several decades, which, in combination with rapid growth in metal use, means that recycled metal flows will meet only a modest portion of demand for many years to come. Second, it does not dis- tinguish between new (yield loss from fabrication and manufacturing) and old (postconsumer) scrap as input material, making it vulnerable to artifi- cially increased rates based solely on preconsum- er sources (fabricators may be given incentives to increase their scrap output to meet secondary de- mand, making recycled content an incentive for inefficiencies in fabrication and manufacturing). What recycled content means to encourage, in- stead, is the amount of old scrap that is collected and processed for recycling [also expressed as old scrap ratio (6)]. The indicator that measures this more directly is the EOL-RR, defined as the fraction of metal in discarded products that is reused in such a way as to retain its functional properties. The EOL-RR depends on the collection rate of end-of-life products and the efficiency of the subsequent separation and pre-processing steps, all involving complex interactions of a wide va- riety of players (7). A United Nations panel re- cently defined and quantified recycling rates for 60 elements (Fig. 1) (8). Two messages jump out at once from the figure. The first is that EOL-RRs for the commonly used “base metals” (iron, cop- per, zinc, etc.) are above 50% (although, as the report is careful to point out, usually not very far above 50%). The second, and striking, impression Center for Industrial Ecology, School of Forestry and Environ- mental Studies, Yale University, New Haven, CT 06511, USA. *To whom correspondence should be addressed. E-mail: barbara.reck@yale.edu 10 AUGUST 2012 VOL 337 SCIENCE www.sciencemag.org690 www.sciencemag.oDownloadedfrom verse mix of elements in modern produbarbara.reck@yale.edu 10 AUGUST 2012 VOL 337 SCIENCE ww690 hly comingled “specialty metals,” re- e so technologically and economically that the attempt is seldom made. dern technology has produced a co- The more intricate the product and verse the materials set it uses, the bet- y to perform, but the more difficult it so as to preserve the resources that al to making it work in the first place. efits of recycling are many, the most ng the potential to reduce the extrac- in ores, thus extending ose resources. The envi- mpacts of metal produc- uced substantially when aterials rather than pri- als are used (9), and re- metal is generally much y-efficient than acquiring ne (10–13). Depending on nd the form of scrap, re- ave as much as a factor of energy consumption (14). nfluencing the recycling re the volumes involved nomic value of the me- hat are typically used in ies (enabling economies resent the largest fraction recycled metals. These h occur in relatively pure e straightforward to re- steel, aluminum, copper, and nickel. Their EOL- ove 50%, and the life- products in which they en span several decades. nfrastructures are well lenging. Here, collection rates fluctuate around just 50% in developed countries, largely as the result of exports of used vehicles to developing countries with minimal recycling technology (18). The same factors are also involved in the meager 5 to 10% recycling of platinum group metals in electronics (19). Within developing countries, informal recycling and low-technology processing combine to sharply limit the recovery of precious metals from consumer products (20). Hazardous metals recycling takes place only tential for recycling depends on approaches and actions taken at each stage of the life cycle. This can be illustrated by example (Fig. 2A). The left panel shows the 2005 global life cycle for nickel (27). Of the 650 Gg (thousands of metric tons) of nickel that were discarded from use, about two-thirds was returned. Together with manufac- turing scrap (165 Gg of Ni), recycled nickel pro- vides about one-third of the nickel required for fabrication and manufacturing—obviously well worth doing, but with the potential for further im- 21 21 1817161514131211 10 Ne 9 F 8 O 7 N 6 C 5 B 4 Be 3 Li HeH 1817161514131211 10 Ne 9 F 8 O 7 N 6 C 5 B 4 Be 3 Li HeH 36 Kr 35 Br 34 Se 33 As 32 Ge 31 Ga 30 Zn 29 Cu 28 Ni 27 Co 26 Fe 25 Mn 24 Cr 23 V 22 Ti 21 Sc 20 Ca 19 K 18 Ar 17 Cl 16 S 15 P 14 Si 13 Al 12 Mg 11 Na 36 Kr 35 Br 34 Se 33 As 32 Ge 31 Ga 30 Zn 29 Cu 28 Ni 27 Co 26 Fe 25 Mn 24 Cr 23 V 22 Ti 21 Sc 20 Ca 19 K 18 Ar 17 Cl 16 S 15 P 14 Si 13 Al 12 Mg 11 Na 86 Rn 85 At 84 Po 83 Bi 82 Pb 81 Tl 80 Hg 79 Au 78 Pt 77 Ir 76 Os 75 Re 74 W 73 Ta 72 Hf *56 Ba 55 Cs 54 Xe 53 I 52 Te 51 Sb 50 Sn 49 In 48 Cd 47 Ag 46 Pd 45 Rh 44 Ru 43 Tc 42 Mo 41 Nb 40 Zr 39 Y 38 Sr 37 Rb 86 Rn 85 At 84 Po 83 Bi 82 Pb 81 Tl 80 Hg 79 Au 78 Pt 77 Ir 76 Os 75 Re 74 W 73 Ta 72 Hf *56 Ba 55 Cs 54 Xe 53 I 52 Te 51 Sb 50 Sn 49 In 48 Cd 47 Ag 46 Pd 45 Rh 44 Ru 43 Tc 42 Mo 41 Nb 40 Zr 39 Y 38 Sr 37 Rb 717069686766656463626160595857* L th id 717069686766656463626160595857* L th id 118 Uuo (117) (Uus) 116 Uuh 115 Uup 114 Uuq 113 Uut 112 Uub 111 Rg 110 Ds 109 Mt 108 Hs 107 Bh 106 Sg 105 Db 104 Rf **88 Ra 87 Fr RnAtPoBiPbTlHgAuPtIrOsReWTaHfBaCs 118 Uuo (117) (Uus) 116 Uuh 115 Uup 114 Uuq 113 Uut 112 Uub 111 Rg 110 Ds 109 Mt 108 Hs 107 Bh 106 Sg 105 Db 104 Rf **88 Ra 87 Fr RnAtPoBiPbTlHgAuPtIrOsReWTaHfBaCs 103 Lr 102 No 101 Md 100 Fm 99 Es 98 Cf 97 Bk 96 Cm 95 Am 94 Pu 93 Np 92 U 91 Pa 90 Th 89 Ac ** Actinides 71 Lu 70 Yb 69 Tm 68 Er 67 Ho 66 Dy 65 Tb 64 Gd 63 Eu 62 Sm 61 Pm 60 Nd 59 Pr 58 Ce 57 La * Lanthanides 103 Lr 102 No 101 Md 100 Fm 99 Es 98 Cf 97 Bk 96 Cm 95 Am 94 Pu 93 Np 92 U 91 Pa 90 Th 89 Ac ** Actinides 71 Lu 70 Yb 69 Tm 68 Er 67 Ho 66 Dy 65 Tb 64 Gd 63 Eu 62 Sm 61 Pm 60 Nd 59 Pr 58 Ce 57 La * Lanthanides <1% 1-10% >10-25% >25-50% >50% Fig. 1. Global estimates of end-of-life recycling rates for 60 metals and metalloids, circa 2008 [adapted from (6)]. onAugust13,2012www.sciencemag.orgDownloadedfrom
- 19. REVIEW Challenges in Metal Recycling Barbara K. Reck* and T. E. Graedel Metals are infinitely recyclable in principle, but in practice, recycling is often inefficient or essentially nonexistent because of limits imposed by social behavior, product design, recycling technologies, and the thermodynamics of separation. We review these topics, distinguishing among common, specialty, and precious metals. The most beneficial actions that could improve recycling rates are increased collection rates of discarded products, improved design for recycling, and the enhanced deployment of modern recycling methodology. As a global society, we are currently far away from a closed-loop material system. Much improvement is possible, but limitations of many kinds—not all of them technological—will preclude complete closure of the materials cycle. T he generation now between the ages of 20 and 30 is, in many parts of the world, the first to have grown up with the re- cycling bin as a normal part of life. Discarded paper, cans, and bottles have designated places to go, and often go there. The situation is less cer- tain for products used for a number of years be- fore being discarded—computers, refrigerators, automobiles—for which recycling procedures have been diverse and sporadic. And few know what happens to obsolete equipment used on behalf of individuals but owned by corporations or organizations—medical imaging machines, air- craft engines, and the like. The recycling of products in the “occasional- ly discarded” or “owned by somebody else” cat- egories is complicated by the rapid expansion of the designer’s materials palette that has taken place in the past several decades (1, 2). Today, virtually every stable element in the periodic ta- ble is used so as to take advantage of its unique physical and chemical properties. The result is that many products are more functional and reli- able than before. An unintended consequence is that recycling has become much more compli- cated and challenging. Several reviews of metal recycling have ap- peared in recent years (3–5). They discuss central issues such as recycling technologies, economic limitations, and methods of enhancement. Some open questions still remain: How much is going on, and what are the trends? What are its limits? Is a closed-loop materials economy possible? It is these systems-level topics that are the focus of the present work. The Current Status of Metal Recycling How well is the world doing at recycling the di- verse mix of elements in modern products? Two metrics answer this question best: recycled con- tent and end-of-life recycling rate (EOL-RR). Re- cycled content describes the share of scrap in metal production, which is important to get a sense of the magnitude of secondary supply. This indicator, however, has two limitations. First, life- times of metal-containing products often span several decades, which, in combination with rapid growth in metal use, means that recycled metal flows will meet only a modest portion of demand for many years to come. Second, it does not dis- tinguish between new (yield loss from fabrication and manufacturing) and old (postconsumer) scrap as input material, making it vulnerable to artifi- cially increased rates based solely on preconsum- er sources (fabricators may be given incentives to increase their scrap output to meet secondary de- mand, making recycled content an incentive for inefficiencies in fabrication and manufacturing). What recycled content means to encourage, in- stead, is the amount of old scrap that is collected and processed for recycling [also expressed as old scrap ratio (6)]. The indicator that measures this more directly is the EOL-RR, defined as the fraction of metal in discarded products that is reused in such a way as to retain its functional properties. The EOL-RR depends on the collection rate of end-of-life products and the efficiency of the subsequent separation and pre-processing steps, all involving complex interactions of a wide va- riety of players (7). A United Nations panel re- cently defined and quantified recycling rates for 60 elements (Fig. 1) (8). Two messages jump out at once from the figure. The first is that EOL-RRs for the commonly used “base metals” (iron, cop- per, zinc, etc.) are above 50% (although, as the report is careful to point out, usually not very far above 50%). The second, and striking, impression Center for Industrial Ecology, School of Forestry and Environ- mental Studies, Yale University, New Haven, CT 06511, USA. *To whom correspondence should be addressed. E-mail: barbara.reck@yale.edu 10 AUGUST 2012 VOL 337 SCIENCE www.sciencemag.org690 owww.sciencemag.orgDownloadedfrom verse mix of elements in modern producbarbara.reck@yale.edu 10 AUGUST 2012 VOL 337 SCIENCE ww690 Fig. 2. (A) The global cycles of nickel for the year 2005 [left, adapted from (27)] and neodymium for 2007 [right, adapted from (16)]. The numbers indi- cate flows of metal within the anthropo- sphere, in Gg (thousands of metric tons). Flows crossing the dotted line transfer metal to the anthropogenic cycle or vice versa. The width of the arrows is an ap- proximate indication of flow magnitude. Min, mining; S, smelting; R, refining; F, fabrication of semi-products (rolls, sheets, etc.); Mfg, manufacturing; W, waste man- agement and recycling. (B) Material ef- ficiencies across nickel’s life cycle. Of the extracted nickel, 82% enters fabrication, manufacturing, and end use; 65% enters the recycling processes; and 52% is re- cycled for another use in which nickel’s properties are required (functional recy- cling). Losses across one life cycle amount to 48%. EOL-RR, end-of-life recycling rate; NFR, nonfunctional recycling. EOL-RR 63% To nickel recycling100% 82% 65% 52% Nickel 17% Landfill 13% NFR Nickel extraction Losses 18% Tailings & slag 100% 82% 65% 52% Nickel extraction B Scrap in W S U Min MfgF 14.0 17.3 15.6 21.6 18.9 4.3 1.7 1.6 0.7 1.2 1.2 4.2 4.2 9.8 17.7 stock Scrap in W S U Mfg F R Min 1235 1380 1270 1585 1570 205 85 15 15 650 585 165 55 135 35 105 1805 5 410 stock 30 No old scrap flow! A Ore NFR Landfill Ore Landfill 920 onAugust13,2012emag.org
- 20. France (2014): 142 Mtoe EU (28, 2014): 1606 Mtoe
- 21. Non-renewables & energy conundrum Less concentrated minerals Extraction of raw materials requires more energy Energy is less accessible More raw materials are necessary to produce energy
- 22. C H N O P S Living systems share the same chemistry
- 23. An 8-Bit, 40-Instructions-Per-Second Organic Microprocessor on Plastic Foil Kris Myny, Student Member, IEEE, Erik van Veenendaal, Gerwin H. Gelinck, Jan Genoe, Member, IEEE, Wim Dehaene, Senior Member, IEEE, and Paul Heremans Abstract—Forty years after the ﬁrst silicon microprocessors, we demonstrate an 8-bit microprocessor made from plastic electronic technology directly on ﬂexible plastic foil. The operation speed is today limited to 40 instructions per second. The power consump- tion is as low as 100 W. The ALU-foil operates at a supply voltage of 10 V and back-gate voltage of 50 V. The microprocessor can execute user-deﬁned programs: we demonstrate the execution of the multiplication of two 4-bit numbers and the calculation of the moving average of a string of incoming 6-bit numbers. To execute such dedicated tasks on the microprocessor, we create small plastic circuits that generate the sequences of appropriate instructions. The near transparency, mechanical ﬂexibility, and low power con- sumption of the processor are attractive features for integration on everyday objects, where it could be programmed as, amongst other items, a calculator, timer, or game controller. Index Terms—Dual-gate, ﬂexible circuits, ﬂexible micropro- cessor, ﬂexible processor, organic circuits, organic microprocessor, organic processor, organic transistor, plastic circuits, plastic microprocessor, plastic processor. I. INTRODUCTION ELECTRONICS pervades everyday life and is undeniably making its way from computing to telephony and to as- sisting us in everyday tasks through products such as electronic paper to read and write, electronic noses to sense gases, smart lighting with electronics to save energy, and so on. The key en- abler of these pervasive electronics applications is the fact that integration of ever more transistors with ever smaller dimen- sions has resulted in the cost of a single semiconductor tran- sistor, or switch, to dwindle to the level of ten nano-dollars per transistor. Nevertheless, if the cost of a transistor in a chip is negligible and decreasing, the cost of placing and routing elec- tronics on daily objects is not necessarily proportionally low. Manuscript received May 07, 2011; revised July 17, 2011; accepted September 05, 2011. Date of publication November 04, 2011; date of current Plastic electronics refers to the technology to make transistors and circuits with thin-ﬁlm organic or plastic semiconductors on arbitrary substrates, including not only rigid substrates such as glass, but also ﬂexible plastic foils. A variety of organic molecules and polymers have been developed as semiconduc- tors, and the best ones [1]–[4] today feature a charge carrier mobility on the order of 1–10 cm /Vs, some 100 to 1000 times lower than that of silicon. When integrated into circuits, the realistic mobility values are somewhat lower but nevertheless sufﬁcient for applications such as backplanes for ﬂexible active-matrix displays, in particular for ﬂexible electronic papers [5]. The ﬁrst dedicated circuit applications of organic thin-ﬁlm transistors have also appeared in recent years, such as recently demonstrated by the integration of an organic line driver for an organic active matrix OLED display [6]. Such cir- cuits can be made directly on thin and ultra-ﬂexible plastic foils, which allows them to be very simply laminated on everyday objects, and furthermore provides appealing characteristics in terms of bending radius and robustness: we no longer talk of ﬂexible electronics but of truly crinkable electronics [7]. Here, we investigate the possibility to use this technology to realize microprocessors on plastic foil. As the cost of an elec- tronic chip decreases with production volume, ultralow-cost mi- croprocessors on easy-to-integrate ﬂexible foils will be an en- abler for ambient intelligence: one and the same type of chip can be integrated on vastly different types of objects to perform customized functions, such as identiﬁcation, simple computing, and controlling. The organic microprocessor has been implemented as two different foils: an arithmetic and logic unit (ALU) foil and an instruction foil. The ALU-foil is a general-purpose foil which can execute a multitude of functions. On the other hand, the in- struction foil is a dedicated chip that generates the sequence of instructions to obtain a speciﬁc function. It sends this sequence IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 47, NO. 1, JANUARY 2012 Instructions-Per-Second Organic processor on Plastic Foil E, Erik van Veenendaal, Gerwin H. Gelinck, Jan Genoe, Member, IEEE, aene, Senior Member, IEEE, and Paul Heremans microprocessors, we rom plastic electronic he operation speed is The power consump- es at a supply voltage e microprocessor can rate the execution of the calculation of the numbers. To execute we create small plastic ropriate instructions. y, and low power con- res for integration on Plastic electronics refers to the technology to make transistors and circuits with thin-ﬁlm organic or plastic semiconductors on arbitrary substrates, including not only rigid substrates such as glass, but also ﬂexible plastic foils. A variety of organic molecules and polymers have been developed as semiconduc- tors, and the best ones [1]–[4] today feature a charge carrier mobility on the order of 1–10 cm /Vs, some 100 to 1000 times lower than that of silicon. When integrated into circuits, the realistic mobility values are somewhat lower but nevertheless sufﬁcient for applications such as backplanes for ﬂexible active-matrix displays, in particular for ﬂexible electronic Mostly organic materials (carbon based) Au for gates
- 24. An 8-Bit, 40-Instructions-Per-Second Organic Microprocessor on Plastic Foil Kris Myny, Student Member, IEEE, Erik van Veenendaal, Gerwin H. Gelinck, Jan Genoe, Member, IEEE, Wim Dehaene, Senior Member, IEEE, and Paul Heremans Abstract—Forty years after the ﬁrst silicon microprocessors, we demonstrate an 8-bit microprocessor made from plastic electronic technology directly on ﬂexible plastic foil. The operation speed is today limited to 40 instructions per second. The power consump- tion is as low as 100 W. The ALU-foil operates at a supply voltage of 10 V and back-gate voltage of 50 V. The microprocessor can execute user-deﬁned programs: we demonstrate the execution of the multiplication of two 4-bit numbers and the calculation of the moving average of a string of incoming 6-bit numbers. To execute such dedicated tasks on the microprocessor, we create small plastic circuits that generate the sequences of appropriate instructions. The near transparency, mechanical ﬂexibility, and low power con- sumption of the processor are attractive features for integration on everyday objects, where it could be programmed as, amongst other items, a calculator, timer, or game controller. Index Terms—Dual-gate, ﬂexible circuits, ﬂexible micropro- cessor, ﬂexible processor, organic circuits, organic microprocessor, organic processor, organic transistor, plastic circuits, plastic microprocessor, plastic processor. I. INTRODUCTION ELECTRONICS pervades everyday life and is undeniably making its way from computing to telephony and to as- sisting us in everyday tasks through products such as electronic paper to read and write, electronic noses to sense gases, smart lighting with electronics to save energy, and so on. The key en- abler of these pervasive electronics applications is the fact that integration of ever more transistors with ever smaller dimen- sions has resulted in the cost of a single semiconductor tran- sistor, or switch, to dwindle to the level of ten nano-dollars per transistor. Nevertheless, if the cost of a transistor in a chip is negligible and decreasing, the cost of placing and routing elec- tronics on daily objects is not necessarily proportionally low. Manuscript received May 07, 2011; revised July 17, 2011; accepted September 05, 2011. Date of publication November 04, 2011; date of current Plastic electronics refers to the technology to make transistors and circuits with thin-ﬁlm organic or plastic semiconductors on arbitrary substrates, including not only rigid substrates such as glass, but also ﬂexible plastic foils. A variety of organic molecules and polymers have been developed as semiconduc- tors, and the best ones [1]–[4] today feature a charge carrier mobility on the order of 1–10 cm /Vs, some 100 to 1000 times lower than that of silicon. When integrated into circuits, the realistic mobility values are somewhat lower but nevertheless sufﬁcient for applications such as backplanes for ﬂexible active-matrix displays, in particular for ﬂexible electronic papers [5]. The ﬁrst dedicated circuit applications of organic thin-ﬁlm transistors have also appeared in recent years, such as recently demonstrated by the integration of an organic line driver for an organic active matrix OLED display [6]. Such cir- cuits can be made directly on thin and ultra-ﬂexible plastic foils, which allows them to be very simply laminated on everyday objects, and furthermore provides appealing characteristics in terms of bending radius and robustness: we no longer talk of ﬂexible electronics but of truly crinkable electronics [7]. Here, we investigate the possibility to use this technology to realize microprocessors on plastic foil. As the cost of an elec- tronic chip decreases with production volume, ultralow-cost mi- croprocessors on easy-to-integrate ﬂexible foils will be an en- abler for ambient intelligence: one and the same type of chip can be integrated on vastly different types of objects to perform customized functions, such as identiﬁcation, simple computing, and controlling. The organic microprocessor has been implemented as two different foils: an arithmetic and logic unit (ALU) foil and an instruction foil. The ALU-foil is a general-purpose foil which can execute a multitude of functions. On the other hand, the in- struction foil is a dedicated chip that generates the sequence of instructions to obtain a speciﬁc function. It sends this sequence IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 47, NO. 1, JANUARY 2012 Instructions-Per-Second Organic processor on Plastic Foil E, Erik van Veenendaal, Gerwin H. Gelinck, Jan Genoe, Member, IEEE, aene, Senior Member, IEEE, and Paul Heremans microprocessors, we rom plastic electronic he operation speed is The power consump- es at a supply voltage e microprocessor can rate the execution of the calculation of the numbers. To execute we create small plastic ropriate instructions. y, and low power con- res for integration on Plastic electronics refers to the technology to make transistors and circuits with thin-ﬁlm organic or plastic semiconductors on arbitrary substrates, including not only rigid substrates such as glass, but also ﬂexible plastic foils. A variety of organic molecules and polymers have been developed as semiconduc- tors, and the best ones [1]–[4] today feature a charge carrier mobility on the order of 1–10 cm /Vs, some 100 to 1000 times lower than that of silicon. When integrated into circuits, the realistic mobility values are somewhat lower but nevertheless sufﬁcient for applications such as backplanes for ﬂexible active-matrix displays, in particular for ﬂexible electronic C MICROPROCESSOR ON PLASTIC FOIL 285 (a) (b) tion of the dual-gate OTFT technology. (b) Typical measured transfer characteristic of this OTFT ( m/ m) when using the ntrol gate (right). (Figures from [11].) TABLE II COMPARISON WITH THE EARLY SILICON PROCESSOR Such very low po spread mobile ap To conclude, the ﬁrst plastic m sors made in p-ty ago.1 Signiﬁcant eters such as gat but some marked of the plastic tec slower than the e of the three-orde ganic semicondu consumption is a ture implementat ides [17] could b with still very att performance, and This work wa and TNO in the f [1] P. T. Herwi thesis, solid- effect transis [2] J. H. Chen, C. Haughn, chains on th J. Mater. Ch [3] N. Kobayash
- 25. “From the point of view of the mathematician the property of being digital should be of greater interest than that of being electronic.” Alan Turing Lecture to the London Mathematical Society on 20 February 1947
- 26. A r I 1A lIl* f n | | closed circuit, and the symbol 1 ( A Symbolic t6tnaiysis or ue ay and represent the hindrance of an o cuit. Thus when the circuit a-b Xab and when closed Xa Swi chi, Circuits Two hindrances Xab and Xc said to be equal if whenever th a-b is open, the circuit c-d is op By CLAUDE E. SHANNON whenever a-b is closed, c-d is ENROLLED STUDENT AIEE Now let the symbol + (plus) be to mean the series connection of I. Introduction bolic study of logic. For the synthesis terminal circuits whose hindra problem the desired characteristics are added together. Thus Xab + X N THE CONTROL and protective first written as a system of equations, and hindrance of the circuit a-d when * circuits of complex electrical systems the equations are then manipulated into are connected together. Simil it is frequently necessary to make in- the form representing the simplest cir- product of two hindrances X, tricate interconnections of relay contacts cuit. The circuit may then be immedi- more briefly XabXc, will be de and switches. Examples of these cir- ately drawn from the equations. By mean the hindrance of the circui cuits occur in automatic telephone ex- this method it is always possible to find by connecting the circuits a-b an changes, industrial motor-control equip- the simplest circuit containing only parallel. A relay contact or sw ment, and in almost any circuits designed series and parallel connections, and in be represented in a circuit by th to perform complex operations auto- some cases the simplest circuit containing in figure 1, the letter being matically. In this paper a mathematical any type of connection. responding hindrance function. analysis of certain of the properties of Our notation is taken chiefly from 2 shows the interpretation of such networks will be made. Particular symbolic logic. Of the many systems in sign and figure 3 the multiplicat attention will be given to the problem of common use we have chosen the one This choice of symbols makes network synthesis. Given certain char- which seems simplest and most suggestive nipulation of hindrances very s acteristics, it is required to find a circuit for our interpretation. Some of our ordinary numerical algebra. incorporating these characteristics. The phraseology, as node, mesh, delta, wye, It is evident that with the ab solution of this type of problem is not etc., is borrowed from ordinary network nitions the following postulates w unique and methods of finding those par- Postulates department of electrical engineering at MaWssachu- wtt setts Institute of Technology, Cambridge. This able, paper is an abstract of a thesis presented at MIT for the degree of master of science. The authoris the indebted to Doctor F. L. Hitchcock, Doctor cuit Vannevar Bush, and Doctor 5. H. Caldwell, all of MIT, for helpful encouragement and criticism, used 1938, VOL. 57 preprintingMay27, 1938. ~~sociate a symbol Xai or more simply X, necessary t department of electrical engineering at MaWssachu- wtthtemnlaadb.Tivn- both. The setts Institute of Technology, Cambridge. This able, a function of time, will be called which differ paper is an abstract of a thesis presented at MIT for the degree of master of science. The authoris the hindrance of the two-terminal cir- However, t indebted to Doctor F. L. Hitchcock, Doctor cuit a-b. The symbol 0 (zero) will he tions in Vannevar Bush, and Doctor 5. H. Caldwell, all of MIT, for helpful encouragement and criticism, used to represent the hindrance of a symbols. 1938, VOL. 57 Shannon-Relay Circuits ....The + Y .. ..The series connection of circuits X Y ....................The proposition which is true if either X or Y different viewpoint. Instead of associating Xab Y....The parallel connection of circuiits X and Y.................... The proposition which is true if both X and Y prpoiretiowthathe circuita-tab is open. Then all are true the symbols are directly interpreted as propositions .Tlhe circuit which is open when X is closed, .....................Thecontradictory of proposition X and the operations of addition and multiplication and closed when X is open will be seen to represent series and parallel connec- . The circuits open and close simultaneously....................Each proposition implies the other tions. 14 Shannon-Relay Circuits AIFE TRANSACTIONS protective first written as a system of equations, and hindrance of the circuit a-d when b and c al systems the equations are then manipulated into are connected together. Similarly the make in- the form representing the simplest cir- product of two hindrances X,,b'X,4 or ay contacts cuit. The circuit may then be immedi- more briefly XabXc, will be defined to these cir- ately drawn from the equations. By mean the hindrance of the circuit formed ephone ex- this method it is always possible to find by connecting the circuits a-b and c-d in rol equip- the simplest circuit containing only parallel. A relay contact or switch will ts designed series and parallel connections, and in be represented in a circuit by the symbol ions auto- some cases the simplest circuit containing in figure 1, the letter being the cor- thematical any type of connection. responding hindrance function. Figure operties of Our notation is taken chiefly from 2 shows the interpretation of the plus Particular symbolic logic. Of the many systems in sign and figure 3 the multiplication sign. problem of common use we have chosen the one This choice of symbols makes the ma- rtain char- which seems simplest and most suggestive nipulation of hindrances very similar to nd a circuit for our interpretation. Some of our ordinary numerical algebra. tics. The phraseology, as node, mesh, delta, wye, It is evident that with the above defi- lem is not etc., is borrowed from ordinary network nitions the following postulates will hold: those par- least num- Postulates tch blades 1. a. 0 *0 = 0 A closed circuit in parallel with a closed circuit is a closed ll also be circuit. ber of cir- b. 1 + 1 = I An open circuit in series with an open circuit is an open rcuit in all circuit. t will be 2. a. 1 + 0 = 0 + 1 = 1 An open circuit in series with a closed circuit in either well-known order (i.e., whether the open circuit is to the right or left works have of the closed circuit) is an open circuit. b. O * 1 = 1 0 = O A closed circuit in parallel with an open circuit in either in relay order is a closed circuit. se are the 3. a. 0 + 0 = 0 A closed circuit in series with a closed circuit is a closed formations, circuit. b. 1 1 = 1 An open circuit in parallel with an open circuit is an open these prob- circuit. as follows: 4 At any given time either X c 0 or X = 1. y a set of equations..rel atn theory for similar concepts in switching These are sufficient to develop all the circuits. theorems which will be used in connection calculus is with circuits containing only series and these equa- II Series-Parallel parallel connections. The postulates are processes, Two-Terminal Circuits arranged in pairs to emphasize a dualityo ordinary relationship between the operations of calculus is FUNDAMENTAL DEFINITIONS addition and multiplication and the ous to the AND POSTULATES quantities zero and one. Thus, if in in the sym- We shall limit our treatment to cir- any of the a postulates the zero's are re- cuits containing only relay contacts and placed by one's and the multiplications by the AIEE switches, and therefore at any given time by additions and vice versa, the cor- basic sciences the circuit between any two terminals responding b postulate will result. This r convention, Manuscript must be either open (infinite impedance) fact is of great importance. It gives available for or closed (zero impedance). Let us as- each theorem a dual theorem, it being ~~sociate a symbol Xai or more simply X, necessary to prove only one to establish X sitions, it is evident that a perfect X(YZ) = (X Y)Z (2b) XX' = 0 (6b) analogy exists between the calculus for X(Y + Z) = XY + XZ (3a) °' = 1 (7a) switching circuits and this branch of X(Y +7b)Z)=XY + xz(3asymbolic logic.** The two interpreta- X + YZ = (X + Y)(X + Z) (3b) 1' = 0 (7b) tions of the symbols are shown in table I. 1-X = X (4a) (X')Y = X (8) Due to this analogy any theorem of the calculus of propositions is also a true o + X-X (4b) ANALOGUE WITH THE theorem if interpreted in terms of relay 1 + X = 1 (5a) CALCULUS OF PROPOSITIONS circuits. The remaining theorems in O-X = O (5b) We are now in a position to demon- this section are taken directly from this strate the equivalence of this calculus with field. For example, to prove theorem 4a, certain elementary parts of the calculus De Morgan's theorem: note that X is either 0 or 1. If it is 0, of propositions. The algebra of logie1-3 (X + Y + Z . .) = X'. Y'.Z' (9a) the theorem follows from postulate 2b; originated by George Boole, is a sym- if 1, it follows from postulate 3b. Theo- bolic method of investigating logical (X-Y-Z Y)' = X' + Y'+ Z'+ rem 4b now follows by the duality princi- relationships. The symbols of Boolean (9b) ple, replacing the 1 by 0 and the - by +. algebra admit of two logical interpreta- This theorem gives the negative of a sum Due to the associative laws (2a and 2b) tions. If interpreted in terms of classes, or product in terms of the negatives of parentheses may be omitted in a sum or the variables are not limited to the two the summands or factors. It may be product of several terms without am- possible values 0 and 1. This interpre- easily verified for two terms by substitut- biguity. The z and H symbols will be tation is known as the algebra of classes- ing all possible values and then extended used as in ordinary algebra. If, however, the terms are taken to rep- to any number n of variables by mathe- The distributive law (3a) makes it resent propositions, we have the calculus matical induction. possible to "multiply out" products and of propositions in which variables are A function of certain variables X1, to factor sums. The dual of this theorem limited to the values 0 and 1,* as are the X2 .X is any expression formed from the variables with the operations of addition, multiplication, and negation. Table 1. Analogue Between the Calculus of Propositions and the Symbolic Relay Analysis 1. For all numbered references, see list at end of Symbol Interpretation in Relay Circuits Interpretation in the Calculus of Propositions paper. * This refers only to the classical theory of the calculus of propositions. Recently some work has X....The circuit X ....................Theproposition X been done with logical systems in which proposi- 0....The circuit is closed .................... The proposition is false tions may have more than two "truth values." 1....The circuit is open .................... The proposition is true ** This analogy may also be seen from a slightly X + Y .. ..The series connection of circuits X Y ....................The proposition which is true if either X or Y different viewpoint. Instead of associating Xab XY....The parallel connection of circuiits X and Y.................... The proposition which is true if both X and Y prpoiretiowthathe circuita-tab is open. Then all are true the symbols are directly interpreted as propositions .Tlhe circuit which is open when X is closed, .....................Thecontradictory of proposition X and the operations of addition and multiplication and closed when X is open will be seen to represent series and parallel connec- . The circuits open and close simultaneously....................Each proposition implies the other tions. 714 Shannon-Relay Circuits AIFE TRANSACTIONS n variables may be expanded requiring the most elements using any E nth variable as follows: type of circuit has not as yet been deter- ||,mined. S XI ut .. Xn) =f=Xnf(Xi.. Xn_-1, 1) + ined. 1S Xn'f(XI. . . Xn-1, 0) (19) DUAL NETWORKS R T 2 IR' , Yt terms f(Xi . . . X_1, 1) and The negative of any network may be | w Q,O'X,,-1, 0) are functions of n-I found by De Morgan's theorem, but the L,Q<0- Z J ZI zY3and if they individually require network must first be transformed into an Figure 21 (left). Simple constant-voltageelements for n -1 variables, then equivalent series-parallel circuit (unless Simuire the most elements for n it is already of this type). A theorem will system providing there is no other be developed with which the negative of Figure 22 (right). Constant-current system f writingf so that less elements any planar two-terminal circuit may be equivalent to figure 21 red. We have assumed that found directly. As a corollary a method elements for n - 1 variables are of finding a constant-current circuit path represents a path across M dividing y vXn and its negative. If we, the circuit from c to d along which every1 element of M is one. Hence XCd = 1. substitute forf(Xi... Xn- 1, l) SE | Similarly, ifXC = 0, then X0b = 1, ion 02 Xk and for f(X ... | y2 and it follows that Xab = Xcd.1 n It is evident from this theorem that a the function we find: egative for any planar network may be
- 27. Obsolete architecture ? Binary numbers Boolean logic Arithmetic computation Data processing Information processing
- 28. Obsolete architecture ? Higher frequencies means more instructions per second. More instructions per seconds means more energy dissipation. Higher densities of transistors means more instructions per second. Higher densities of transistors means reaching the physical limits of semiconductors
- 29. 7.2 Grand challenges 7.2.1 Computational state variable(s) other than solely electron charge 7.2.2 Non-thermal equilibrium systems 7.2.3 Novel Energy Transfer Interactions 7.2.4 Nanoscale Thermal Management 7.2.5 Sub-lithographic Manufacturing Process 7.2.6 Alternative Architectures
- 30. Towards a radical biomimetic approach ? Materials do matter: they are crucial, embodiment. Living systems are sustainable because they are connected to biogeochemical cycle. We must invent technologies that are connected to the biogeochemical cycle. This implies that everything must be re-invented from materials to architectures and processes We may only have about a century, maybe even less, to do so…

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