nanoscale self-assembly a computational view
DESCRIPTION
Nanoscale Self-Assembly A Computational View. Philip Kuekes Quantum Science Research HP Labs. What’s Cooking? Everybody likes Recipes. Two Challenges for Nanoelectronics. Invent a new switching device Develop a new fabrication process Examine Architecture First. - PowerPoint PPT PresentationTRANSCRIPT
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Nanoscale Self-Assembly
A Computational View
Philip KuekesQuantum Science Research
HP Labs
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What’s Cooking?
Everybody likes Recipes
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•Invent a new switching device
•Develop a new fabrication process
Examine Architecture First
Two Challenges for Nanoelectronics
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HPL Teramacmulti-architecture computer
• 106 gates operating at 106 cycle/sec
• 100 times workstation performance
• Largest defect-tolerant computer ever built
• 220,000 (3%) defective components
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Defect Theology
• Original Sin• Redemption Through Good Works• Guilt by Association
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Redundant Testing
PASS
FAIL
PASS
PASS
PASS
PASS
FAIL
PASSPASS PASS
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Defect Tolerance for Free
• CMOS Technology –Configuration bit >20 x wire crossing area
• Molecular Technology –Configuration bit smaller than wire crossing
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Teramac Crossbar ArchitectureMemory0
Switch
Teramac crossbar
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OO
ONN
N
OO
O
NN
N
O
OO
OO
O
OO
OO
4PF6-
CH2OH
+ +
++
Rotaxane Molecular Switch -Prof. Fraser Stoddart, UCLA
C.P. Collier, E.W. Wong et al.
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Experimental Realization of aExperimental Realization of a Molecular-Tunneling Switch Molecular-Tunneling Switch
-10
-5
0
5
10
Cur
rent
(A
)
-2.0 -1.0 0.0 1.0
Voltage (V)
Ti
PtDevice =
Molecule + Electrodes
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Moletronics Architecture
• Wires• Memories• Logic• Integrated Circuits
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Crossbar at 17 nm half-pitch width
Smallest virus 30-42 nmhepatitis B
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Parallel ErSi2 wires grown by self-assembly2 nm width with a nine nanometer separation
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Logic Array DesignU V W X Y Z
a
b
c
d
e
f
Y = (U AND V) OR (W AND X)
Z = V+ C = V-
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MOLECULAR SWITCH LATCH: EXPT DATA
DDatainput
Clock /control
C1 C2
QDataout
SW1 SW2E
-0.5
-0.25
0
0.25
0.5
Dat
a (V
)
Test 1 input +0.5V out -0.46V
2
1
0
-1
Con
trols
(V)
-2
-1
0
1
C1
C2
-500
0
500
Cur
rent
(uA
)
-1 0 1Voltage (V)
SW1 200
100
0
-100
-1 0 1Voltage (V)
SW2
-0.5
-0.25
0
0.25
0.5
Dat
a (V
)
121086420Time (s)
Test 2 input -0.5V out +0.50V
RES
ET
SET
1SE
T 2
ENA
BLE
RES
TOR
E&
INVE
RT
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Expt: Latch works!
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
Volta
ge (V
)
Input Output
Trial 1
2
3
4
5
6
Signal restorationInversion, if desired>100mV operating margin
No nanoscale transistor!
J. Appl. Phys. Feb 1, 2005
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Random Demultiplexer
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COH
OH3C
Pt
TiAl
Pt
TiAl
SiO2
Pt
TiAl
VLB
Si
‘C20
’
-20
-10
0
10
Cur
rent
den
sity
(103 A
/cm
2 )
-2 -1 0 1Voltage (V)
C20_1
C20_2
C20_3
2002
20031
64 2004
1 k
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5Vo
ltage
(V)
Input Output
Trial 1
2
3
4
5
6
(ITRS 2018)
[ 0 0
0 ]
[ 1 0
0 ]
[ 0 1
0 ]
[ 0 0
1 ]
[ 1 1
0 ]
[ 1 0
1 ]
[ 0 1
1 ]
[ 1 1
1 ]
[ 0 0
0 ]
Boolean inputs [ A B C ]
100
0
Outp
ut Vo
ltage
(mV)
VT
( A · C ) + B
10 July 2001 7 Jan 2004
Output
VA
DrivingJunction A
R
DrivingJunction B
ReceivingJunction C
VB VC
Figure 1. A 1×3 array of inverting hysteretic resistor latches. This tiny serial logic array is sufficient for implementation of a NAND gate.
Output
VA
DrivingJunction A
R
DrivingJunction B
ReceivingJunction C
VB VC
Output
VA
DrivingJunction A
R
DrivingJunction B
ReceivingJunction C
VB VC
Figure 1. A 1×3 array of inverting hysteretic resistor latches. This tiny serial logic array is sufficient for implementation of a NAND gate.
NAND
2
1
0151050
4
3
2
1
0
-1
Cur
rent
(mA
)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5Voltage (V)
1
23
4
5
6
7
8
9
10
1617
C20
-0.18
-0.16
-0.14
-0.12
-0.10
Cur
rent
(A)
-3 -2 -1 0 1 2Voltage (V)
-1.0
-0.5
0
0.5
1.0
Curre
nt (m
A)
-0.5 0 0.5Voltage (V)
16 k
2005
HP crossbar switches & circuits
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How does a Molecular Computer Grow Up?
• Conventional Computer Teacher
• Low Bandwidth Link• Initially Stupid Molecular
Student
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I Get By With A Little HelpFrom My Friends
• Tutors• Doctors
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Complexity
• Self Assembly & Thermodynamics
• Arbitrary Graphs
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Tradeoffs
• Cost of doing the chemistry
• Cost of doing the computing
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The Pure and the Grubby
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- Expanders
- Cayley Graphs
- Ramanujan Graphs
The Math
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Today
• Physical Scientists can only do very simple self-assembly
• Mathematicians can create interesting complex structures with very simple generators
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The new capability
• Combine the simple physical processes with the mathematical constructions
• Nanoscale self-assembled systems with enough complexity to do useful computation.
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The Physics
• Self-Assembled DNA Nanostructures • Self-Assembled Surface Chemistry • Viral Self-Assembly • Molecular Electronic Circuit Assembly • DNA-linked Nano-particle Structures
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The MathAdvantages of Simple
Construction
• amenable to self-assembly • short explicit description• highly-connected• sparse
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Physical StructuresNot Just Abstract Graphs
• defect-tolerance• efficiently embedded in three-dimensional
space • relatively short edge-lengths.
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•Local rules
•Global structure
Algorithmic Manufacturing
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•Computer Code
•Biology
• Chemistry, Physics, Materials Science
Feedback and the Way Forward
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•Computer Code
•Biology
• Chemistry, Physics, Materials Science Reaction Diffusion
Feedback and the Way Forward
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Stealing from Biology
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DNA and Proteinsversus Cells
Logic Design as Geometry
Spatial Structure
Controlled diffusion
Compartments as wires
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Organelles
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Garbage Collection
Ubiquitin
Apoptosis
Mass transport
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The Best of Both Worlds
Self-assembly
Adaptive External Programming
Self-disassembly
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Tradeoffs
• Cost of doing the chemistry
• Cost of doing the computing