Bayesian Macromodeling for
Circuit Level QCA Design
Saket Srivastava and Sanjukta Bhanja
Department of Electrical Engineering
University of South Florida, Tampa
IEEE

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Table of Contents
Purpose of this work
Overview of Quantum

Dot Cellular Automata
Overview of Bayesian Modeling
Layout Level Bayesian Modeling
Bayesian Macro modeling
Thermal Studies with Macro models
Circuit Level Modeling
Experimental Results
Conclusion
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Purpose of this work
Fast Bayesian Computing Model.
Abstracts the behavior of circuit components in QCA
design using Probabilistic Macromodeling.
Quick Estimation and Comparison of Quantum
Mechanical Quantities in QCA architecture at Layout
level and Circuit level.
Directly models the quantum mechanical steady state
probabilities at a hierarchical level.
Can be used to identify weak spots in the design in
the early design process, at the circuit level itself.
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Prior Work
S. Henderson, E. Johnson, J Janulis, and P. Tougaw,
“incorporating standard cmos design process methodologies into
the QCA logic design process”, IEEE Transactions on
Nanotechnology, vol.3, pp. 2

9, March 2004.
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Quick Overview

Quantum

Dot Cellular Automata
In a QCA cell two electrons occupy diagonally opposite dots in
the cell due to
mutual repulsion
of like charges.
A QCA cell can be in any
one of the two possible states
depending on the polarization of charges in the cell.
P = +1
P =

1
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Quick Overview

Quantum

Dot Cellular Automata
Electrostatic Interaction
between charges in two QCA Cells is
given as:
P = +1
P =

1
E
kink
= E
opp. polarization
–
E
same
polarization
This interaction is determines the
kink energy
between two cells.
Kink energy
is the is the Energy cost of two neighboring cells
having opposite polarization.
P = +1
P = +1
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Quick Overview

Bayesian Modeling
Bayesian Network
is a DAG
in which:
Nodes:
Random variables
Links
:
Causal dependencies amongst
random variables.
General representation:
Minimal factored Joint Probability
Distribution function:
Joint Probability Distribution function:
(a) QCA Majority Gate
Each Node has a
Conditional
Probability Table
(CPT) that quantifies
the effect of parents on that node.
(b) Bayesian model
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Quick Overview

Bayesian Modeling
The steady state polarization
of a QCA cell is obtained from the
Hamiltonian matrix using Hartree approximation and is given by:
E
k
is the kink energy.
γ
is the tunneling energy.
f
i
is the geometric distance factor.
is the weighted sum of
neighborhood polarizations.
ρ
ss
is the steady state polarization.
P
The probabilities of observing the system in each of the two states
is given as:
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Layout Level Bayesian Model of Cell
Arrangements
We then determine the parent and child nodes of each QCA cell.
Each QCA cell is represented as a random variable (node) taking on two possible
values.
Conditional Probabilities for each cell (node) is then given by:
where:
and
also known as Rabbi frequency.
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Bayesian Macro modeling
A
macromodel
is a functional block containing a small number of cells.
The macromodels of different circuit elements are the conditional probability of
output cells given the values of the input cells.
It can be obtained by marginalizing the joint probability distribution of those cells
over all the remaining cells in a layout.
For example if a macromodel block contains three cells (x
i
,x
j
and x
k
) out of
n
cells in
a layout, then its joint probability is obtained by:
is the joint probability distribution
over all n cells in a layout
where:
To compute the marginal probabilities for each macromodel, we first transform the
DAG into a junction tree of cliques and then the marginal probabilities are calculated
using local message passing between cliques.
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Bayesian Macro modeling
Figure:
Validation of the Bayesian network modeling of
QCA circuits with Hartree
Fock approximation based
coherence vector
based quantum mechanical simulation of
same circuit. Probabilities of correct output are compared
for basic circuit elements.
Symbol
Macromodel
MAJ
Simple Majority
CM
Clocked Majority
INV
Inverter
LINE
Line
IC
Inverter Chain
CO
Corner
ET
Even Tap
OT
Odd Tap
CB
Crossbar
AND
And Gate
OR
Or Gate
Table 1:
Abbreviations for Macromodel
blocks used in the circuit design of Adder

1
and Adder

2.
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Thermal Studies with Macromodels
Macromodel
QCA Layout
Bayesian Model
Block Diagram
Thermal Behavior
(a) Majority
Gate
1 clock zone
3 inputs
1 output
(a) Clocked
Majority Gate
2 clock zones
3 inputs
1 output
(a) Inverter
1 clock zone
1 input
1 output
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Thermal Studies with Macromodels
As the Temperature increases, the
polarization probability
at
the output node decreases.
Thermal behavior is also
dependent on the input vector set
.
A clocked majority gate consists of two clocking zones as it has
been seen that
circuit reliability increases
when majority gates
are clocked separately from the outputs.
This is done in order to
synchronize the input signals
reaching
the majority gate irrespective of the path length they have
traversed.
Larger number of cells in clocked majority lead to
overall
increased uncertainty
that accounts for a larger drop in
polarization at the output node at higher temperature.
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Thermal Studies with Macromodels
Macromodel
QCA Layout
Bayesian Model
Block Diagram
Thermal Behavior
(a) Majority
Gate
1 clock zone
3 inputs
1 output
(a) Clocked
Majority Gate
2 clock zones
3 inputs
1 output
(a) Inverter
1 clock zone
1 input
1 output
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Circuit Level Modeling
–
A NAND Gate Example
AND
INV
LINE
A
B
Out
A
B
Out
The QCA layout
of a NAND gate
consists of a Majority gate with one fixed
cell, an Inverter and a Line and in three
clock zones.
The Macromodel circuit
of a NAND
gate is modeled using the macromodel
blocks of an AND gate, an Inverter and a
Line.
A Bayesian network
of the macromodel
circuit is then formed.
A
B
Out
A QCA layout Bayesian model
is also
developed.
The two models
are then studied and a
compared for output node polarization at
different temperatures.
Out
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Circuit Level Modeling
–
Full Adder

2
QCA layout of Adder

2
Bayesian network for of Adder

2 layout
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Circuit Level Modeling
–
Full Adder

2
Macromodel block design of Adder

2
Bayesian model for macromodel circuit design
of Adder

2
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Circuit Level Modeling
–
Full Adder

2
Probability of correct output for sum and carry of Adder
2 based on the layout
level
Bayesian net model and the circuit level macromodel, at different temperatures, for
different inputs
(a)
(0,0,0)
(b)
(0,0,1)
(c)
(0,1,0)
(d)
(0,1,1).
(a)
(d)
(c)
(b)
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Circuit Level Modeling
–
2x2 Multiplier
Macromodel block design of 2x2 Multiplier
QCA layout of 2x2 Multiplier
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Circuit Level Modeling
–
2x2 Multiplier
Bayesian model for macromodel circuit design of 2x2 Multiplier
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Circuit Level Modeling
–
2x2 Multiplier
Probability of correct output at the four output nodes of 2x2 Multiplier circuit based on the
layout

level Bayesian net model and the circuit level macromodel, at different
temperatures, for different inputs (a)(1,0),(0,1) (b) (1,0),(1,1) (c) (1,1),(0,1) (d) (1,1),(1,1).
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Experimental Results
Table 3:
Comparison between simulation timing of a Full Adder circuits and 2x2
Multiplier circuit in
QCADesigner(QD)
and
Genie Bayesian Network(BN) Tool
for Full Layout and Macromodel Layout.
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Experimental Results
–
Error Modes
•
We compute the near

ground state configurations that
results in error in the output carry bit
C
out
of a QCA full
using both the layout and circuit level models.
•
We show the case for input vector set (1,0,0). The other
input vector sets have similar results.
•
We use red marker to point to the components that are
weak (high error probabilities) in both the layout and circuit
level.
•
If a node (a macromodel block) in macromodel circuit
layout is highly error prone for a given input set, then some
or all the QCA cells forming that macromodel block are
highly prone to error.
•
This indicates that weak spot in the design can be
identified early in the design process, at the circuit level
itself.
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Conclusion
We proposed an efficient
Bayesian Network based probabilistic macro modeling
strategy for QCA circuit.
This model can estimate cell polarizations, ground state probability, and lowest

energy error state probability, without the need for computationally expensive
quantum

mechanical computations.
We showed that the polarization estimates at layout and circuit levels are in good
agreement.
We illustrated a full adder design and a 2

bit multiplier design.
We showed that the weak spots at the layout level can be effectively identified at the
circuit level using this model.
One possible future direction of this work involves the extension of the BN model to
handle sequential logic.
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Thank You
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