Kind: captions
Language: en
the following is a conversation with
jeff shaneline a scientist at nist
interested in opto electronic
intelligence
we have a deep technical dive into
computing hardware that will make jim
keller proud i urge you to hop on to
this rollercoaster ride through
neuromorphic computing and
superconducting electronics and hold on
for dear life
jeff is a great communicator of
technical information and so it was
truly a pleasure to talk to him about
some physics and engineering
to support this podcast please check out
our sponsors in the description
this is the lex friedman podcast and
here is my conversation with jeff
shaneline i got a chance to read a
fascinating paper you um authored called
optoelectronic intelligence
so maybe we could start by talking about
this paper and start with the basic
questions what is optoelectronic
intelligence
yeah so in that paper the the concept i
was trying to describe is
sort of an architecture for building
brain-inspired computing
that leverages light for communication
in conjunction with
electronic circuits for computation
in that particular paper a lot of the
work we're doing right now in our
project at nist is focused on
superconducting electronics for
computation i'll go into
why that is but
that might make a little more sense in
context if we first
describe what that is in contrast to
which is semiconducting electronics
so is it worth taking a couple minutes
to describe
semiconducting electronics
it might even be worthwhile to step back
and uh talk about electricity and
circuits and how circuits work right
before we talk about super conductivity
right okay
how does the computer work jeff well i
won't go into everything that makes a
computer work but
let's talk about the
basic building blocks a transistor so
and even more basic than that a
semiconductor material silicon say so
uh in silicon silicon is a semiconductor
and what that means is at low
temperature there are no free charges no
free electrons that can move around so
when you talk about electricity you're
talking about
predominantly electrons moving to
establish electrical currents and they
move under the influence of voltages so
you apply voltages
electrons move around those can be
measured as currents and you can
represent information in that way so
semiconductors are special
in the sense that
they are really malleable so if you have
a semiconductor material
it you can change the number of free
electrons that can move around by
putting different
elements different atoms in lattice
sites so
what is a lattice site well a
semiconductor is a crystal which means
all the atoms that comprise the material
are
at exact locations that are perfectly
periodic in space so if you start at any
one atom and you go along the what are
called the lattice vectors you get to
another atom and another atom and
another atom and for high quality
devices it's important that it's a a
perfect crystal with very few defects
but you can intentionally replace a
silicon atom with say a phosphorus atom
and then you can you can change the
number of free electrons that are in a
region of space that has that excess of
what are called dopants so picture a
device that has a left terminal and a
right terminal
and if you apply a voltage between those
two you can cause electrical current to
flow between them
now we
add a third terminal up on top there and
depending on the voltage between the
left and right terminal and that third
voltage you can you can change that
current so what's commonly done in
digital electronic circuits is to
leave a fixed voltage from left to right
and then
change that voltage that's applied at
what's called the gate the gate of the
transistor so
what you do is you you make it to where
there's an excess of electrons on the
left excess of electrons on the right
and very few electrons in the middle and
you do this by changing the
concentration of different dopants in
the lattice spatially
and then when you apply a voltage to
that gate you can either cause current
to flow or turn it off and so that's
sort of your zero and one you if you
apply voltage current can flow that
current is representing a digital one
and uh from that from that basic element
you can build up
all the complexity of digital electronic
circuits that
have
really had a profound influence on our
society now you're talking about
electrons can you give a sense of what
scale we're talking about when we're
talking about in silicon
uh being able to mass manufacture these
kinds of uh gates
yeah so scale in a number of different
senses well at the scale of the silicon
lattice the distance between two atoms
there is half a nanometer so
um
people often like to compare these
things to the the width of a human hair
i think it's some
six orders of magnitude smaller than the
width of a human hair
uh something on that order
so remarkably small we're talking about
individual atoms here and electrons are
of that length scale when they're in
that environment
but there's another sense that scale
matters in digital electronics this is
perhaps the more important sense
although they're related scale refers to
a number of things it refers to
the size of that transistor so for
example i said you have a left contact a
right contact and some space between
them where the
the gate electrode sits that that's
called the the channel width uh or the
channel length and um
what has enabled what we think of as
moore's law or the continued
increased performance in silicon
microelectronic circuits is the ability
to make that size that feature size ever
smaller ever smaller at a
a
really remarkable pace i mean
that that feature size has
decreased uh consistently every couple
of years for
the since the 1960s and that was
that was what moore predicted in the
1960s he thought it would continue for
at least two more decades and it's been
much longer than that and so
um
that is why we've been able to fit ever
more devices ever more transistors ever
more computational power on essentially
the same size of chip
so a user sits back and does essentially
nothing you're running the same computer
program but those devices are getting
smaller so they get faster they get more
energy efficient and all of our
computing performance just continues to
improve and we don't have to
think too hard about
what we're what we're doing as say a
software design or something like that i
absolutely don't mean to say that
there's no innovation in
software that are the user side of
things of course there is but
from from the hardware perspective we
just have been given this gift of
continued performance improvement
through this scaling that is ever
smaller feature sizes with very similar
um say power consumption that power
consumption is
has not continued to scale in the most
recent decades but um
nevertheless we had a really good run
there for a while and now we're down to
gates that are seven nanometers which is
state of the art right now maybe
global foundries is trying to push it
even lower than that i can't keep up
with
where the predictions are that it's
going to end but seven nanometer
uh
seven nanometer transistor
has just just a few tens of atoms along
the length of the conduction pathway so
a naive
semiconductor device physicist would
think you can't go much further than
that without
some kind of revolution in the way we
think about the physics of our devices
is there something to be said about
the mass manufacture of these devices
right right so that's another thing so
how have we been able to make those
transistors smaller and smaller well
companies like intel global foundries
they invest a lot of money in the
lithography so how are these
chips actually made well one of the most
important steps is this what's called
ion implantation so you have you start
with sort of a pristine silicon crystal
and then using photolithography which is
a technique where you can pattern
different shapes using light
you can define which regions of space
you're going to implant with different
uh different species of ions that are
going to change the local electrical
properties right there
so by using ever shorter wavelengths of
light and different kinds of optical
techniques and different kinds of
lithographic techniques things that go
far beyond
my knowledge base
you can just simply shrink that feature
size down and you say you're at seven
nanometers well the wavelength of light
that's being used is over 100 nanometers
that's already deep in the uv so
how how are those minut features
patterned well there's there's an
extraordinary amount of innovation that
has gone into that but nevertheless it
stayed very consistent in this
ever-shrinking feature size and now the
question is can you make it smaller and
even if you do do you still continue to
get performance improvements but that's
another kind of scaling where
these these companies have been able to
so okay you you picture a chip that has
a processor on it well that chip is not
made as a chip it's made as a on a wafer
and um
using photolithography you basically
print the same pattern
on different dies all across the wafer
multiple layers tens probably
probably a hundred some layers in a
mature foundry process and and you do
this on ever bigger wafers too that's
another aspect of scaling that's
occurred in the last several decades so
now you have this 300 millimeter wafer
it's like as big as a pizza and it has
maybe a thousand processors on it and
then you dice that up using a saw and
now you can sell these things
so cheap because the the manufacturing
process was so streamlined i think a
technology as revolutionary as silicon
microelectronics
has to have that kind of
manufacturing scalability which i will
just emphasize i believe is
enabled by
physics it's not i mean that of course
there's human ingenuity that goes into
it but at least from my
my side where where i sit it sure looks
like the physics of our universe allows
us to to produce that and we've we've
discovered how
more so than we've invented it although
of course we have invented it humans
have invented it but it was it's almost
as if it was there waiting for us to to
discover it you mean the entirety of it
or are you specifically talking about
the techniques of photo lithography like
the optics involved i mean the entirety
of the scaling down to the seven
nanometers that you're able to have
electrons
not interfere with each other in such a
way that
you could still have gates like that's
enabled to achieve that scale spatial
and temporal
seems to be very special and is enabled
by the physics of our world all the
things you just said so starting with
the the silicon material itself
silicon is a unique semiconductor it has
essentially ideal properties for making
a specific kind of transistor that's
extraordinarily useful so
i mentioned that
silicon has uh well when you make a
transistor you have this gate contact
that sits on top of the conduction
channel and depending on the voltage you
apply there you pull more carriers into
the conduction channel or push them away
so it becomes more or less conductive
in order to have that work without just
sucking those carriers right into that
contact you need a very thin insulator
and and part of scaling has been to
gradually decrease the thickness of that
of that gate insulator so that you can
use a roughly similar voltage and still
have the same
current voltage characteristics so the
material that's used to do that or i
should say was initially used to do that
was a silicon dioxide which just
naturally grows on the silicon surface
so you expose silicon to
the atmosphere that we breathe and
uh well if you're manufacturing you're
going to
purify these gases but nevertheless that
that what's called a native oxide will
grow there there are essentially no
other materials on the entire periodic
table that have as good of a
gate insulator as as that silicon
dioxide and that that has to do with
nothing but the physics of the
interaction between silicon and oxygen
and if it wasn't that way transistors
could not
they they could not perform in
nearly the the degree of capability that
they have and that that has to do with
the way that the the oxide grows the
reduced density of defects there it's
it's insulation meaning essentially it's
energy gaps you can apply a very large
voltage there without having current
leak through it so that's physics right
there
um
there are other things too silicon is a
semiconductor in in an elemental sense
you you only need silicon atoms a lot of
other semiconductors you need two
different kinds of atoms like a compound
from group three and a compound from
group five that opens you up to lots of
defects that can occur where one atom's
not sitting quite at the lattice site it
is and it's switched with another one
that degrades performance
but then also on the side that you
mentioned with the the manufacturing
we have access to light sources that can
produce these very short wavelengths of
light
how does photolithography occur well you
actually put this polymer on top of your
wafer
and you expose it to light and then you
use a
aqueous chemical processing to dissolve
away the regions that were exposed to
light and leave the regions that were
not
and
we are blessed with these polymers that
have the right property where
they can um
cause scission events where the polymer
splits where a photon hits i mean you
know maybe maybe that's not too
surprising but i don't know it all it
all comes together to have this
really complex uh manufacturable
ecosystem where
very sophisticated technologies can be
devised
and it works quite well
and amazingly like you said with a
wavelength at like 100 nanometers or
something like that you're still able to
achieve on this polymer precision of
whatever whatever we said seven
nanometers yeah i think i've heard like
four nanometers being talked about
something like that yes
i if we could just pause on this and
we'll return to super connectivity but
in this whole journey from a history
perspective what what do you think is
the most beautiful
at the intersection of engineering and
physics to you and this whole process
that we talked about with silicon and
photolithography
things that people were able to achieve
in order to uh
push
the moore's law forward is it the early
days the the invention of the transistor
itself is it uh some particular cool
little thing that um
maybe not many people know about like
what do you think is most beautiful in
this
in this whole process journey
the most beautiful is a little difficult
to answer let me let me try and sidestep
it a little bit and just say
what strikes me about looking at the the
history of
silicon microelectronics is that
uh so when when quantum mechanics was
developed people quickly began applying
it to semiconductors and it was
broadly understood that these are
fascinating systems and people cared
about them for their basic physics but
also
their utility is devices and then the
transistor was invented in the late 40s
in a
relatively crude experimental setup
where you just crammed a metal electrode
into the semiconductor and and that was
that was ingenious these people were
able to um
make it work you know uh
but so
what what i want to get to that that
really strikes me is that in those early
days
there were a number of different
semiconductors that were being
considered they had different properties
different strengths different weaknesses
most people thought germanium was the
the way to go
it it had some some nice properties uh
related to things
about how the electrons move inside the
lattice
but other people thought that compound
semiconductors with group 3 and group 5
also had really really extraordinary um
properties that might be conducive to to
making the best devices so there were
different groups exploring each of these
and that's great that's how science
works you have to cast a broad net but
then
what i what i find striking is why why
is it that silicon
won because it's not that it's not that
germanium is a useless material and it's
not present in technology or compound
semiconductors they're both
doing
doing exciting and important things
slightly more niche applications whereas
silicon is the semiconductor material
for microelectronics which is the
platform for digital computing which has
transformed our world why did silicon
win it's because of a remarkable
assemblage of qualities
that no one of them was the clear winner
but it it made these sort of compromises
between a number of different influences
it had that really excellent
gate oxide that allowed it to that
allowed us to make mosfets these high
performance transistors
so quickly and cheaply and easily
without having to do a lot of materials
development the the band gap of silicon
um
is actually so in a semiconductor
there's there's an important parameter
which is called the band gap which tells
you uh if you they're they're sort of
electrons that fill up to one level in
in the energy
diagram and then there's a gap where
electrons aren't allowed to have an
energy in a certain range and then
there's another energy level above that
and that that difference between the
lower sort of filled level and the
unoccupied level that tells you how much
voltage you have to apply in order to
induce a current to flow
so with germanium that's about 0.75
electron volts that means you have to
apply 0.75 volts to to get a current
moving
and it turns out that
if you compare that to the
the thermal excitations that are induced
just by the temperature of our
environment that gap's not quite big
enough you start to
use it to perform computations it gets a
little hot and you get all these
accidental carriers that are excited
into the the conduction band and it
causes errors in your computation
silicon's band gap is just a little
higher 1.1
electron volts but you have an
exponential dependence on the
the number of carriers that are present
that can induce those errors
uh it decays exponentially with that
voltage so just that that slight extra
energy in that band gap
really puts it in an ideal position to
be operated
in the in the conditions of our of our
ambient environment it's kind of
fascinating that so like you mentioned
air is um decrease exponentially
uh with the voltage so
it's funny because this error thing
comes up you know when you start talking
about quantum computing
it's kind of amazing that everything
we've been talking about the errors as
we scale down seems to be extremely low
yes and like all of our computation
is based on the assumption that it's
extremely low yes so it's not digital
computation digital sorry digital
computation so as opposed to our
biological computation our brain is like
the assumption is stuff is gonna fail
all over the place and we somehow have
to still be robust to that that's
exactly right so this also this is gonna
be the most controversial part of our
conversation where you're gonna make
some enemies so let me ask because we've
been talking about physics and
engineering
a which group of people is smarter and
more important for this one
let me ask the question in a better way
some of the big innovations some of the
beautiful things that we've been talking
about how much of it is physics how much
of it is engineering my dad is a
physicist and he talks down to all the
amazing engineering that we're doing
in
the artificial intelligence and the
computer science and the robotics and
all that space so we argue about this
all the time so what do you think who
gets more credit i'm genuinely not
trying to just be politically correct
here i don't see how you would have
any of the
what we consider sort of the great
accomplishments of society without both
and you absolutely need both of those
things physics tends to play a key role
earlier in the development and then
engineering optimization these things
take over
and uh i mean
the invention of the transistor or
actually even before that the
understanding of semiconductor physics
that allowed the invention of the
transistor that's all physics so if you
didn't have that physics you don't even
get to get on the on the on the field
but
once you have
understood and demonstrated that this is
in principle possible
moore's law is engineering that
why we have uh
computers more powerful than
than old supercomputers in each of our
phones is that's all engineering and
i i think i would
be quite foolish to say that
that's
i mean that that's not valuable if it's
not a great contribution uh it's a
beautiful dance would you put like
silicon
the understanding of the material
properties
in the space of engineering like how
does that whole process work to
understand that it has all these nice
properties or even the development of
photolithography
is is that basically
would you put that in the category of
engineering
no i would say that
it is basic physics it is applied
physics it's material science it's um
x-ray crystallography it's polymer
chemistry it's it's everything i mean
chemistry even is thrown in there
absolutely yes yes absolutely just no
biology
okay we can get to biology right well
the biology is in the humans that are
engineering the system that's all
integrated deeply okay so let's return
you mentioned this uh word
superconductivity
so what does that have to do
with what we're talking about right okay
so in a semiconductor as i
tried to describe a second ago
you can sort of uh
in induce currents by applying voltages
and those have sort of typical
properties that you would expect from
some kind of a conductor those electrons
they don't just flow
perfectly without dissipation if an
electron collides with an imperfection
in the lattice or another electron it's
going to slow down it's going to lose
its momentum so you have to keep
applying that voltage in order to keep
the current flowing in a superconductor
something different happens if you get a
current to start flowing it will
continue to flow indefinitely there's
there's no dissipation so
that's crazy how does that happen well
it happens at low temperature and this
is crucial it has to it has to be a
quite low temperature and
what what i'm talking about there i
for essentially all of our conversation
i'm going to be talking about
conventional superconductors um
sometimes called low tc superconductors
low critical temperature superconductors
and so
those materials have to be
in at a temperature around say around 4
kelvin i mean their critical temperature
might be 10 kelvin something like that
but you want to operate them at around 4
kelvin 4 degrees above absolute zero and
what happens at
that temperature at that very low
temperatures in certain materials is
that the the noise of
atoms moving around the lattice
vibrating electrons colliding with each
other that becomes sufficiently low that
the electrons can settle into this very
special state it's sometimes referred to
as a macroscopic quantum state because
if i had a piece of
superconducting material here let's say
niobium is a very typical
um
superconductor if i if i had a block of
niobium here and we cooled it below its
critical temperature
all of the electrons in that in that
superconducting state would be in one
coherent quantum state they would
the the wave function of that state
is described in terms of all of the
particles simultaneously but it extends
across macroscopic dimensions the size
of a whatever material the size of
whatever
block of that material i have sitting
here and the way that the way this
occurs is that
you know we let's try to be a little bit
light on the technical details but
essentially the electrons coordinate
with each other they they are able to
in this macroscopic quantum state
they're able to sort of
one can quickly take the place of the
other you can't tell electrons apart
they're they're what's known as
identical particles so if this electron
runs into a
defect that would otherwise cause it to
scatter
it can just sort of
um
almost miraculously avoid that defect
because it's not really in that location
it's part of a macroscopic quantum state
and the entire quantum state was not
scattered by that defect so you can get
a current that flows without dissipation
and that's called a supercurrent
that's
uh
sort of just very much scratching the
surface of of superconductivity there's
very deep and rich physics there which
is probably not
the main subject we need to go into
right now but it turns out that when you
have
this material you can you can do usual
things like make wires out of it so you
can get current to flow in a straight
line on a chip but you can also make
other
devices that perform
different kinds of operations some of
them are kind of logic operations like
you like you'd get in a transistor the
most common or most um
i would say
diverse in its utility the component is
a joseph's injunction it's not analogous
to a transistor in the sense that if you
apply a voltage here it changes how much
current flows from left to right but it
is analogous in sort of a sense of
it's the it's the go-to component that a
that a circuit engineer is going to use
to start to build up more complexity so
these are uh these junctions serve as
gates
they can they can serve as gates they
can
so i'm not sure how house
um concerned to be with semantics but
let me just briefly say what a joseph's
injunction is and we can talk about
different ways that they can be used
basically if you have a superconducting
wire and then a small
gap of
a different material that's not
superconducting an insulator or normal
metal and then another superconducting
wire on the other side that's a joseph's
injunction so it's sometimes referred to
as a superconducting weak link so you
have this
superconducting state on one side and on
the other side and that the
superconducting wave function
actually tunnels across that gap and
when you when you create such a physical
entity it has very unusual
um
current voltage
characteristics within in that gap
like like weird stuff through the entire
circuit so you can imagine suppose you
had a loop set up that had one of those
weak links in in the loop
current would flow in that loop
independent even if you hadn't applied a
voltage to it and that's called the
josephson effect so the fact that
there's this
phase difference in the quantum wave
function from one side of the tunneling
barrier to the other induces current to
flow so how does you change state right
exactly so how do you change state now
picture
if i have a
current bias coming down this line in my
circuit and there's a joseph's
injunction right in in the middle of it
and now i make another wire that goes
around the joseph's injunction so i have
a loop here a superconducting loop
i can add
current to that loop by exceeding the
critical current of that joseph's
injunction so
like any superconducting material
it can carry this supercurrent that i've
described this current that can
propagate without dissipation up to a
certain level and if you try and pass
more current than that through the
material it's going to become a
resistive material a normal normal
material so in the in the joseph's
injunction the same thing happens i can
bias it above its critical current and
then what it's going to do it's going to
add a
quantized amount of current into that
loop and what i mean by quantized is
it's going to come in discrete packets
with a well-defined value of current so
in the
vernacular of of some people working in
this community
you would say
you pop a flux on into the loop so a
flux on you pop a flux on into the loop
yeah so if that's a skateboarder
sorry go ahead
a flux on is one of these quantized
uh sort of uh amounts of current that
you can add to a loop and this is a
cartoon picture but i think it's
sufficient for our purposes so which uh
maybe it's useful to say
what is the speed at which these
discrete packets of current travel
because we'll be talking about light a
little bit it seems like the speed is
important the speed is important that's
an excellent question
sometimes i wonder where you
how you became so astute
but um so
this uh matrix four is coming out so
maybe that's related i'm not sure i'm
dressed for the job
i was trying to get to become an extra
matrix for it didn't work out
anyway uh so what's the speed of these
packets you'll have to find another gig
i know i'm sorry um so the speed of the
pack is actually these flux ons these
these uh sort of pulses of of um
current that are generated by joseph's
injunctions they can actually propagate
very close to the speed of light uh
maybe something like a third of the
speed of light that's quite fast so
one of the reasons why joseph's
injunctions are appealing is because
their signals can propagate quite fast
and they can they can also switch very
fast what i mean by switch is perform
that operation that i described where
you add current to the loop
that can happen within um
a few tens of picoseconds so you can get
you can get devices that operate in the
hundreds of gigahertz range and by
comparison
most processors
in our in our conventional computers
operate closer to the the one gigahertz
range maybe three gigahertz seems to be
kind of
where where those speeds have have
leveled out so the gamers listening to
this are getting really excited that
overclock their system to like what is
it like four gigahertz or something 100
this sounds incredible uh can i just as
a tiny tangent is the
physics of this understood well how to
do this stably oh yes the physics is
understood well the physics of joseph's
injunctions is understood well the
technology's understood quite well too
the reasons why
it hasn't displaced silicon
microelectronics in conventional digital
computing
i think are more related to what i was
alluding to before about the the
myriad practical almost mundane aspects
of silicon that make it so useful
you can make a transistor ever smaller
and smaller
and it will still perform its digital
function quite well the same is not true
of a joseph's injunction you really they
don't they just it's not the same thing
that there's this feature that you can
keep making smaller and smaller and
it'll keep performing the same
operations this loop i described any
joseph's in circuit well i i'm going to
be careful i shouldn't say any joseph's
in circuit but many josephs and circuits
the way they process information or the
way they perform whatever function it is
they're trying to do maybe it's sensing
a weak magnetic field
it it depends on an interplay between
the junction and that loop and you can't
make that loop much smaller and it's not
for practical reasons that have to do
with lithography it's for fundamental
physical reasons about the way
the magnetic field interacts with that
superconducting material there's there
are physical limits that no matter how
good our technology got
those circuits would i think would never
be able to be scaled down to the the
densities that silicon microelectronics
can i don't know if we mentioned is
there something interesting about the
various superconducting materials
involved or is it all there's a lot of
stuff that's interesting and it's not
silicon it's not silicon no so like it's
some materials that also required to be
super cold for calvin yes so so let's
dissect a couple of those different
things the super cold part let me just
mention for your gamers out there that
are trying to clock it at four gigahertz
and would love to go to what kind of
cooling system can achieve exactly four
kelvin you need liquid helium and so
liquid helium is expensive it's
inconvenient you need a cryostat that
that sits there and
the energy consumption of that cryostat
is impracticable for it's not going in
your cell phone you're not so you can
picture holding your cell phone like
this and then something the size of you
know uh
a keg of beer or something on your back
to cool it like that makes no sense yeah
so if you if you're trying to make this
in consumer devices uh electronics that
are ubiquitous across society
superconductors are not in the race for
that for now but you're saying so we're
just to frame the conversation maybe the
thing we're focused on is computing
systems that serve as like as servers
like large yes large systems so so then
you can contrast what's going on in your
cell phone with what's going on at
one of the super computers
um
colleague katie schuman invited us out
to oak ridge a few years ago so we got
to see titan and that was when they were
building summits so these are some high
performance supercomputers
out in tennessee and those are filling
entire rooms the size of warehouses you
know so once you're at that level okay
there you're already putting a lot of
power into cooling you need
cooling is part of your engineering task
that you have to deal with so there it's
not entirely obvious that cooling to 4
kelvin is out of the question it's it
has not happened yet and i can speak to
why that is in the digital domain if
you're interested
i think it's not going to happen i don't
think
i don't think superconductors are going
to replace semiconductors
for digital computation
um there are there are a lot of reasons
for that but i think ultimately what it
comes down to is all things considered
cooling
errors
scaling down to feature sizes all that
stuff semiconductors work better at the
system level is there some aspect of uh
just
curious about the historical momentum of
this is there some power to the momentum
of an industry that's mass manufacturing
using a certain material is this is like
a titanic shifting like what's your
sense when a good idea comes along how
good does that idea need to be for the
titanic to start shifting
that's a that's an excellent question
that's an excellent way to to frame it
and you know
i don't know the answer to that but what
i think is okay so the the history of
the superconducting logic
goes back to the 70s ibm made a big push
to do superconducting digital computing
in the 70s and they made some choices
about their
devices and their architectures and
things that
in hindsight were kind of doomed to fail
and i don't mean any disrespect for the
people that did it it was hard to see at
the time but then another generation of
superconducting logic was introduced
i want to say the 90s
someone named likarev and seminov they
propose an entire family of circuits
based on joseph's injunctions that are
doing digital computing based on logic
gates and or
not these kinds of things
um
and they showed how it could go hundreds
of times faster than silicon
microelectronics and it was it's
extremely exciting i wasn't working in
the field at that time but later when i
went back and read the literature i was
just like wow this is this is so awesome
uh and so it you might think well
the reason why it didn't displace
silicon is because silicon already had
so much momentum at that time
but that was the 90s silicon kept that
momentum because it had the simple way
to keep getting better you just make
features smaller and smaller so
you know it would have to be
i don't think it would have to be that
much better than silicon to displace it
but the problem is it's just not better
than silicon it might be better than
silicon in one metric speed of a
switching operation or power consumption
of a switching operation
but building a digital computer is a lot
more than just that elemental operation
it's
everything that goes into it including
the manufacturing including the
packaging including the
um the you know various materials
aspects of things so
the reason why and even in even in some
of those early papers i can't remember
which one it was licorice said something
along the lines of
you can see how we could build an entire
family of digital electronic circuits
based on these components they could go
100 or more times faster than
semiconductor
logic gates
but i don't think that's the right way
to use superconducting electronic
circuits he didn't say what the right
way was but he basically said
digital logic trying to
steal the show from silicon is probably
not what these circuits are are most
suited to accomplish so if we can just
linger and use the word computation
when you talk about computation how do
you think about it do you think purely
on just um the the switching
or do you think something a little bit
larger scale a circuit taken together
performing the basic arithmetic
operations that are then required to do
the kind of
computation that makes up a computer
because when we talk about the speed of
computation is it boiled down to the
basic switching or is there some bigger
picture that you're thinking about well
all right so
maybe we should disambiguate there are a
variety of different kinds of
computation
i don't pretend to be an expert in
the theory of computation or anything
like that i guess it's important to
differentiate though between
digital logic
which represents information as a series
of bits binary digits which you know uh
you can think of them as zeros and ones
or whatever usually they correspond to
a physical system that has two very well
separated states
and then other kinds of computation like
we'll get into more the way your brain
works which
it is i think indisputably processing
information
but
where the computation begins and ends is
not anywhere near as well defined it it
doesn't depend on
these two levels here's a zero here's a
one it's there's a lot of gray area
that's usually referred to as analog
computing
um also
in in conventional digital computers or
um
digital computers in general
you have a concept of what's called
arithmetic depth which is jargon that
basically means how many
sequential operations are performed to
turn
an input into an output and those kinds
of computations in in digital systems
are highly serial
meaning that data streams they don't
branch off too far to the side you do
you have to pull some information over
there and access memory from here and
stuff like that but
by and large the the computation
proceeds in a serial manner
it's not that way in the brain in the
brain
you're always drawing information from
different places it's much more
network-based computing neurons don't
wait for their turn they fire when
they're ready to fire and so it's it's
asynchronous so one of the other things
about a digital system is you're
performing these operations on a clock
and that's a that's a crucial aspect of
it get rid of a clock in a digital
system nothing makes sense anymore the
brain has no clock it builds its own
time scales based on its internal
activity
so so you can think of the brain as kind
of uh
like this like network computation where
it's actually really trivial simple
computers
uh just a huge number of them and
they're networked
i would say it is complex sophisticated
little processors and there's a huge
number of things neurons are not no
offense i don't mean to offend sure no
they're very complicated and beautiful
and yeah but
we often oversimplify them yes they're
actually like there's computation
happening within a neuron right so i i
would say
to think of a a transistor as the
building block of a digital computer is
accurate you use a few transistors to
make your logic gates you build up more
you build up processors from logic gates
and things like that so you can think of
a transistor as a fundamental building
block or you can think of as we get into
more highly parallelized architectures
you can think of a processor as a
fundamental building block to make the
analogy to the
neuro side of things
a neuron is not a transistor a neuron is
a is a processor it has synapses even
synapses are not transistors but they
are more
um they're lower on the information
processing hierarchy in a sense they do
a bulk of the computation but neurons
are entire
processors in and of themselves that can
take in many different kinds of inputs
on many different spatial and temporal
scales and produce many different kinds
of outputs so that they can perform
different computations in different
contexts so this is where it enters this
distinction between computation and
communication
so you can think of neurons performing
computation
and the inter
networking the interconnectivity of
neurons is communication routine neurons
and you see this with very large server
systems i've been i mentioned offline
i've been talking to jim keller whose
dream is to build giant computers that
uh you know
the bottom like there's often the
communication between the different
pieces of computing
so in this paper that we mentioned
optoelectronic intelligence
you say electrons excel at computation
while
light
is excellent for communication
maybe you can linger and say in this
context what do you mean by computation
and communication
what what are electrons what is light
and why do they excel at those two tasks
yeah just to to first speak to
computation versus communication
i would say computation is essentially
taking in
some information
performing operations on that
information and producing new
hopefully more useful information so for
example
um
imagine you have a picture in front of
you
and
there is a key in it and that's what
you're looking for for whatever reason
you want to you want to find the key we
all want to find the key so
the input is that that entire picture
and the output might be the coordinates
where the key is so you've reduced the
total amount of information you have but
you found the useful information for you
in that present moment that's the useful
information you think about this
computation as like controlled
synchronous
sequential not necessarily it could be
that could be how
your system is performing the
computation or it could be
asynchronous it there are lots of ways
to find the key
it depends it depends on the nature of
the data depends on
um that's a very simplified example a
picture with a key in it what about if
you're in the world and you're trying to
decide the best way to
live your life you know that it might be
interactive it might be there might be
some recurrence or some weird
asynchrony i got it so but there's an
input and there's an output and you do
some stuff in the middle that yeah it
goes from the input to the app you've
taken in information and output
different information hopefully reducing
the total amount of information and
extracting what's useful yeah
communication is then
getting that information from the
location in which it's stored because
information is physical as landauer
emphasized and so it is more in one
place and you need to get that
information to another place so that
something else can
use it for whatever computation it's
working on maybe it's part of the same
network and you're all trying to solve
the same problem but neuron a over here
just
deduced something based on its inputs
and it's now sending that information
across the network to another location
so that would be the act of
communication can you linger on landau
and saying information is physical roth
landauer not to be confused with lev
landau
yeah and he he
made huge contributions to our our
understanding of
the reversibility of information in in
this concept that
energy has to be dissipated in computing
when the computation is irreversible but
if you can manage to make it reversible
then you you don't need to expend energy
but if you
um
if you do expend energy to perform a
computation there's sort of a minimal
amount that you have to do and it's kt
log2 and it's all somehow related to the
second law of thermodynamics and that
the universe is an information process
and then we're living in a simulation
so okay sorry sorry for that tangent so
information so that's the defining the
the distinction between computation and
communication
let me say one more thing just to
clarify communication
ideally does not change the information
it moves it from one place to another
but it is preserved
got it okay
all right that's beautiful so
uh then the an electron versus light
distinction and why are electrons
uh good at computation and light good at
communication yes
this is um
there's a lot that goes into it i guess
but just try to speak to the simplest
part of it
electrons interact strongly with one
another they're charged particles so if
i
pile a bunch of them over here
they're feeling a certain amount of
force and they want to they want to move
somewhere else they're strongly
interactive you can also get them to sit
still you can an electron has a mass so
you can you can cause it to
be spatially localized so for
computation that's useful because now i
can make these little devices that put a
bunch of electrons over here and then i
change the the state of
a gate like i've been describing put a
different voltage on this gate and now i
move the electrons over here now they're
sitting somewhere else i have
a physical mechanism with which i can
represent information it's spatially
localized and have knobs that i can
adjust to change where those electrons
are or what they're doing light by
contrast photons of light
uh which are the discrete packets of
energy that were identified by einstein
they do not interact with each other um
especially at low light levels if you're
in a medium and you have a high a bright
high light level
you you can get them to interact with
each other through the interaction with
that medium that they're in but that's
that's a little bit more exotic and for
the purposes of this conversation we can
assume that photons don't interact with
each other so if you have a bunch of
them
all propagating in the same direction
they don't interfere with each other if
i want to send
if i if i have a communication channel
and i put one more photon on it it
doesn't screw up with those other one it
doesn't change what those other ones
were doing at all
so that's really useful for
communication because that means you can
sort of
allow a lot of these photons to flow
uh
with without disruption of each other
and they can they can branch really
easily and things like that but it's not
good for computation because it's very
hard
for
this packet of light to change what this
packet of light is doing they they pass
right through each other so in
computation you want to change
information and if photons don't
interact with each other it's difficult
to get them to change the information
represented by the others so that that's
the fundamental difference is is there
also something about
the way they travel through different
materials
or is that just a particular engineering
no it's not that's deep physics i think
so
this gets back to electrons interact
with each other and photons don't so say
say i'm trying to get a
packet of information
from me to you
and we have a wire going between us in
order for me to send electrons across
that wire i first have to raise the
voltage on my end of the wire and that
means putting a bunch of charges on it
and then that that charge packet has to
propagate along the wire and it has to
get all the way over to you there's that
wire is going to have something that's
called capacitance which basically tells
you how much charge you need to put on
the wire in order to raise the voltage
on it and the capacitance is going to be
proportional to the length of the wire
so the longer the the length of the wire
is the more charge i have to put on it
and
the energy required to charge up that
line and move those electrons to you is
also proportional to the capacitance and
goes as the voltage squared so you get
this huge penalty if you if you want to
send
electrons across a wire over appreciable
distances so distance is an important
thing here when you're doing
communication distance is an important
thing so is the number of connections
i'm trying to make
me to you okay one that's not so bad if
i want to now send it to 10 000 other
friends
then then all of those wires are adding
tons of extra capacitance now not only
does it take forever to put the charge
on that wire and raise the voltage on
all those lines but it takes a ton of
power
and
the number 10000 is not randomly chosen
that's roughly how many connections each
neuron in your brain makes so it a
neuron in your brain needs to send 10
000 messages every time it has something
to say you can't do that if you're
trying to
drive electrons from here to 10 000
different places the brain does it in a
slightly different way which we can
discuss how can light achieve the 10 000
connections and why is it um why is it
better in terms of like the energy use
uh required to use light for the
communication of the ten thousand
connections right right so now instead
of trying to send electrons from me to
you i'm trying to send photons so i can
make what's called a guide which is just
a simple piece of a material it could be
glass like an optical fiber or silicon
on a on a chip and i just have to i just
have to inject photons into that
waveguide and independent of how long it
is independent of how many different
connections i'm making it doesn't change
the the voltage or anything like that
that i have to raise up on the on the
wire so if i have one more connection if
i add additional connections i need to
add more light to the waveguide because
those photons need to split and go to
different
paths that makes sense but i don't have
a capacitive penalty that sometimes
these are called wiring parasitics there
are no parasitics associated with light
in that same sense so
well just this might be a dumb question
but
how do i catch a photon on the other end
uh what's is it material is it's with
the polymer stuff you were talking about
for the
for a different application for
photolithography like how do you catch
photo there's a lot of ways to catch a
photon it's not a dumb question it's a
it's a deep and important question that
basically defines a lot of the work that
goes on in our group at nist
one of my group leaders
say woonam has built his career around
these superconducting single photon
detectors so
if you're going to try to sort of reach