6. How is MQCA Clock Applied?

Source: "Room Temperature Magnetic
Quantum Cellular Automata", R. P. Cowburn

The input dots are set by applying a single
magnetic field pulse (using remanence-free
Helmholtz coils) of either 1300 Oe (for a
logic 1) or 2300 Oe (for a logic 0) directed
along the chain of dots, returning to 0 Oe at
the end of the pulse. A weak oscillating magnetic
field of 625-Oe amplitude and 30-Hz
frequency combined with a 210-Oe bias
magnetic field is then applied along the chain
of dots. The magneto-optical signal from the
networks is recorded over several cycles (Fig.
2). When the input dots are set to 0 (magnetization
points to the left), no response is
observed; when the input dots are set to 1
(magnetization points to the right), all of the
networks switch in step with the applied oscillating
field. If one assigns the negative
phase of the oscillating field to a logic 0 and
the positive phase to a logic 1, then the
response of the networks is the logical AND
of the input dot with the oscillating field. 

7. How are multi-phase clocks / multiple clocks applied (in MQCA)?

8. How do solitons work (as part of the clock to propogate the "signal)? / MQCA General
Source: "Quantum Cellular Automata", Scott Harada

In a magnetic QCA (MQCA) network, exchange interactions between
spins within single dots go on to form a giant classical spin. The resulting
magnetization vector of each dot holds the logic information, e.g. logic “0” if the
vector points to the left, and vice-versa for right. Magnetostatic interactions between
adjacent dots propagate the logic state, much like the electrostatic interactions
involved with electronic QCA networks. The real advantage of MQCA however, is
that they have been shown to operate at room temperature.
MQCA are fabricated in a similar way to their electronic counterparts with the
exception that the dots must be constructed from a magnetic material. Input dots are
elongated which introduces shape-anisotropy and with it, the ability to retain field
orientation regardless of adjacent dots whose own orientation may differ. Because no
electrons are involved in this type of network a non-electrical method is required to
measure the output. One way of going about this is to use magneto-optical analysis,
and the Kerr effect (the rotation of plane polarized light after reflection from a
magnetic material). A focussed laser beam can record the total component of
magnetization, assuming the dots lie in a chain, and can also gather information as to
how many dots are switching once calibration has taken place.

The operation of MQCA is slightly more abstract and thus harder to understand than
that of electronic QCA. To begin with the elongated input dot is set by a large
magnetic pulse, positive for logic “1”, negative for logic “0”. A much weaker
oscillating magnetic field with a negative bias is then set up, and acts as a second
input to the system. During the negative phase of the oscillations (which has a larger
magnitude than the positive phase due to the negative bias) a field is produced that is
strong enough to create what is known as a magnetic soliton. These solitons are
analogous to domain walls within bulk magnetic materials and separate dots of left
and right magnetization. Since the magnetic pulse is passed from one dot to the next
the solitons can be thought of as moving objects carrying the signal along the chain.
The most likely point for soliton creation is the far right hand side of the chain since
the last dot has only one nearest neighbour. As the soliton travels from right to left
changing all the dots to logic “0” (negative field) it eventually reaches the elongated
input dot. At this point one of two things can happen, if the input dot itself has
previously been set to logic “0” the positive phase of the oscillating field will not have
sufficient energy to create another soliton and the entire chain will remain at logic “0”
indefinitely. On the other hand if the input dot has been set to logic “1”, a new soliton
is automatically created between the input dot and the first circular dot (whose
5 magnetic fields are facing in opposite directions). The new soliton is then propagated
from left to right switching the chain to logic “1”, and the process continues. Thus the
entire chain switches in step with the oscillating field.

Fig. 7. A shows a soliton between dots P and Q; B shows the result of having the
input dot set to logic state “0”; C shows the formation of a new soliton between the
input dot and the first circular dot when the input dot is set to logic state “1”.

This simple chain of MQCA acts like an AND gate, with the elongated input dot as
one input and the oscillating field as the other. Experimentally the logic “1” output is
oscillatory with the same frequency as that of the input field.

Fig. 8. The results to the left show the switching response of the chain when the input dot is set to logic
“0” ( B) and logic “1” ( C). On the right the response is seen after the removal of the input dot.

In order to verify that actual switching has occurred, the input dot can be removed.
Even though an initial large magnetic pulse is still sent across the chain no apparent
switching response is seen, leading to the conclusion that the input dot is responsible
for the signal seen in figure 8 (left C).
Although this particular setup of MQCA is very simple, much more complex ones can
be built in order to achieve all the logic operations necessary for computation.
Oscillatory fields can be set up with components in both the x and y directions in
order to create networks that can turn corners or fan out in two directions from a
single point. In theory solitons propagate with no energy loss, however slight
imperfections in the circularity of the dots leads to a very small dissipation of energy.
Since the energy is provided by the oscillating field, power gain to overcome these
losses is easily achievable. On the subject of dot imperfections a circularity accuracy
of ±2% is required, tests with multiple networks show that this too is easily
achievable, and in fact the requirement is an order of magnitude less stringent than
that of electronic QCA fabrication.
It appears then, that MQCA networks have great potential for use in microelectronics.
One of the biggest advantages over current FET based circuits is the integration
density that is possible, and perhaps more importantly the potential for even higher
densities in the future. If a single MQCA dot is taken to be analogous to a transistor,
then the network described in this essay has an integration density of 5500
million/cm2, current CMOS technology is around the 10 million/cm2 mark. The
interaction energy between two dots in this network is 200 kT (Boltzmann’s constant;
room temperature). In order to keep thermally induced data errors below
approximately one per year, an interaction energy of at least 40 kT is required. Using
simple scaling laws it can be shown that dots of 20nm are possible before stability
becomes an issue. At this size integration density soars to around 250,000 million/cm2
giving plenty of scope for the future.
Another advantage of MQCA cells is that since very little energy is dissipated when a
dot changes state, the overall power consumption of a microchip would be very low.
Estimates of typical power consumption for a MQCA chip are around 1W whereas
modern CMOS chips consume about 50W or more. At the moment handheld
computer devices and other portable computer applications are constrained by these
huge power demands so MQCA chips would benefit this area. Research into layered
computer architectures is also coming up against the problems caused by large power
consumption. Keeping these chips cool is a major challenge since transistors are
easily damaged by excess temperatures. Heat build up wouldn’t be a problem if
layered MQCA architectures could be developed, and in principle there is no reason
why they couldn’t be.

9. What are some previous approaches to fault-tolerance for nano-architectures (not just MQCA)?

Source:
1. Fault-tolerant techniques for
nanocomputers
K Nikoli´c, A Sadek and M Forshaw

4 common approaches:
1. R-fold multiple redundancy
2. Cascaded triple modular redundancy
3. Von Neumann’s multiplexing method (using NAND gates)
4. Reconfigurable computer techniques

Background: The first two techniques are generalizations of the well-known
triple modular redundancy (TMR) method [1, 2]. At present
this technique is mainly used to correct for transient errors
while a computer is being used. The third technique, which we
call the NAND multiplexing method, was originally proposed
by von Neumann [3] at the time when early computers were
introduced which were notoriously unreliable. Finally, we
develop a simplified theory of reconfigurable computers and
we present derived results. This technique represent a vastly
more sophisticated version of the ‘test–find–replace’ concept
and it is mainly suitable for tackling manufacturing defects
rather than transient errors.

1. R-fold multiple redundancy: like triple-modular redundancy, but instead of 3 
gates working in parallel, we have "R" gates working in parallel.

2. Cascaded triple modular redundancy (TMR): like triple modular redundancy, but 
the redundancy in increased by another level by using three TMR blocks along
with a majority voter (9 redundant gates total). 

3. Von neumann's multiplexing method (NAND gates): 

similar to RMR, but instead of having a majority gate decide
on the proper output, the output is carried on a bundle of
wires, e.g. for a single bit output one would have R wires
(or Nbundle if we use von Neumann’s notation) in a bundle
which carries the output to the next stage. Therefore, in
this method, processing units of any size are replaced by
multiplexed units containing Nbundle number of lines for every
single input and output. Essentially, a multiplex unit consists
of two stages. The first, the executive stage, performs the
basic function of the processing unit in parallel. The second,
the restorative stage, reduces the degradation caused by the
executive stage and thus acts as a non-linear ‘amplifier’ of
the output, see figure 3. An example for the executive stage
given in figure 3 is a simple NAND (two-input gate), but it
could be a unit with an arbitrary number of gates. Now the
signals to and from units are not carried in single lines but in
bundles (Nbundle = 4 in the example in figure 3) and the unit
is replicated the appropriate number of times (there are four
NAND gates in figure 3) If the inputs and processing units are
perfectly reliable then the lines comprising each output should
be identically stimulated (1) or unstimulated (0). However,
due to errors in the input data as well as errors occurring in
the processing of the inputs from faulty devices, not all of the
output lines in each output will be identically stimulated. Thus,
for multiplexed networks, the final outputs are considered to
be 1 if more than (1 − delta)Nbundle lines are stimulated and
0 if less than delta-Nbundle lines are stimulated, where delta is a
critical level that is pre-defined (0 < delta < 0.5).

4. Reconfigurable: 

10. What is "reversible logic"?
Sources:
1. http://66.102.7.104/search?q=cache:H6dgeSWbxykJ:www.zyvex.com/nanotech/reversible.html+%22reversible+logic%22&hl=en
2. 

11. MQCA: Describe clock circuitry

12. MQCA: line of MQCA cells- binary wire or AND gate? (See room-temp MQCA paper, QCA paper by Scott Hadley). How does entire line
of MQCA cells switch with clock signal when input = 1? Does the entire line of MQCA cells change during a single positive clock, or do they change 
one by one (cell by cell)? (Answer: maybe both- they change cell by cell, but it goes all the way down the line all in one positive-or-negative transition 
of the clock?)