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.

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

18. 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.

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
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

20. 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

27. France (2014): 142 Mtoe
EU (28, 2014): 1606 Mtoe

33. 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

34. C H N O P S
Living systems share the same chemistry

36. 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

37. 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

38. “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

40. 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

41. Obsolete architecture ?
Binary numbers
Boolean logic
Arithmetic
computation
Data processing
Information processing

42. 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

45. 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

46. 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…