The document discusses the memristor, a fourth fundamental circuit element discovered by Leon Chua in addition to the resistor, capacitor, and inductor. A memristor is a two-terminal device whose resistance depends on the amount of charge that has passed through it. It retains its resistance even when power is removed, allowing it to "remember" the last resistance level. Potential applications of memristors include non-volatile memory, neural networks, and more energy efficient computing. Experimental solid-state versions have been developed using thin films of titanium dioxide.
3. Introduction
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Every person with a basic knowledge of electronics would
be familiar with the three fundamental circuit elements —
RESISTOR
CAPACITOR
INDUCTOR
These three elements are defined by the relation between
two of the four fundamental circuit variables — current,
voltage, charge and flux.
But, Leon Chua discovered a fourth fundamental circuit
element which he named as
MEMRISTOR [MEMORY + RESISTOR]
4. What is a MEMRISTOR
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Memristor is really a MEMory ResISTOR
Memristor can be defined as a two terminal device
which shows the relation between magnetic flux and
charge
5. Contd..
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A Memristor is a semiconductor whose resistance varies
as a function of flux and charge. This allows it to
“remember” what has passed through the circuit.
Characterized by Memristance.
The flux between the two terminals is a function of the
amount of electric charge q that has passed through the
device .
Ø=f(q)
6. Features of Memristor
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•Retain its resistance level even after power had
been shut down.
•Remember (or recall) the last resistance it had,
before being shut off.
•“Remember” how much current has pass through it.
7. •Microscopic image shows 17 memristors sandwiched between
a single bottom wire that makes contact with one side of the
device and a top wire that contacts the opposite side.
•The devices act as ‘memory resistors’
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8. Physical analogy for a memristor
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•Resistor is analogous to a pipe of fixed diameter
through which water is flowing.
•Water(charge q), input pressure(voltage V), rate of
flow of water(current I).
•Resistance depends on diameter.
•Memristor is analogous to a special kind of pipe
that expands or shrinks when water flows through
it.
9. •If water pressure is turned off, pipe will retain its
most recent diameter, until water is turned back on.
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10. Memristor Pipe
Charge Water
Conductance Cross sectional area
Potential Pressure
When water flows
in opposite
direction the
diameter decreases
(resistance
increases)
When water flows
in one direction
the diameter
increases
(resistance
decreases)
• The pipe is directive in nature.
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11. Memristor and Resistor
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•The way resistor has resistance, memristor has
memristance.
•Same unit -ohm.
•Memristor can be switched to different states.
•Memristor has non linear V-I plot.
16. In 2008 experimental solid state version was reported by R. Stanley
Williams of Hewlett Packard (HP)
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Leon Chua
R. Stanley Williams
17. Appearance of Memristor
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Crossbar architecture and magnified memristive switch having
platinum electrodes and 2 layers of TiO2
18. Memristor Operation
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(a) TiO2-x layer having oxygen deficiencies over insulating TiO2 layer.
(b) Positive voltage applied to top layer repels oxygen deficiencies in to
the insulating TiO2 layer below.
(c) Negative voltage on the switch attracts the positively charged oxygen
bubbles pulling them out of the TiO2.
19. PT TiOv(2-x) PT
TiO2
3
nm
Oxidized
Reduced
2 nm
(-)ve (+)ve
• Applied voltage makes the oxygen vacancies (+ve)
to shift towards the –ve voltage.
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Working
20. • Shift between the layers is permanent in nature.
• It exist even after the voltage has been removed.
• Causes the permanent change in resistance.
• R(TiO2-x) < R(TiO2)
• When w=D, R =RON =Lo
& when w=0, R=ROFF=Hi
w
gh
Contd..
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21. • No need of expensive retooling.
• Nanoimprint lithography.
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Manufacturing
23. MEMRISTORS IN
MACHINE LEARNING
Memristors have shown promise in the field of
machine learning due to their potential for high-
density and energy-efficient memory and
processing .
They can be used in neuromorphic computing
to mimic the synaptic of biological neurons ,
enabling more efficient and brain-like processing.
Research is ongoing , and memristors could
play a significant role in future machine learning
hardware.
24. Applications
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• As a switch.
• As a non volatile memory.
• Booting free computers.
• Can perform logic operations.
• In artificial neural networks.
25. Benefits
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• Would allow for a quicker boot up since information is
not lost when the device is turned off.
• Hard Disk + RAM = MEMRISTOR.
• Uses less energy and produces less heat.
•As non-volatile memory, memristors do not consume
power when idle.
• Density allows for more information to be stored.
26. Conclusion
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•It is sure that Memristor is going to
revolutionaries in the 21st century as radically as
the transistor in the 20th century.
•But Memristor will have to wait a few years like
transistor which had to wait almost a decade after
it’s invention for its popular applications.
27. References
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1) Memristor resistance modulation for analog applications, Tsung
Wen Lee and Janice H Nickel IEEE, electron device letters, vol
33,oct2012.
2) Memristor applications for programmable analog ICs, Sangho Shin
and Kyungmin Kim, IEEE transactions on Nanotechnology, vol
10,2011.
3) Compact models for memristor based on charge flux constitutive
relationships, IEEE, 2010 IEEE Spectrum: The Mysterious Memristor
By Sally Adee.