Proposals for Memristor Crossbar Design and Applications

Proposals for Memristor Crossbar
Design and Applications
Memristors and Memristive Systems Symposium
UC Berkeley November 21, 2008
Blaise Mouttet
George Mason University

Desirable Manufacturing Goals to Ease
Adoption of Memristor Crossbars
• Design should be compatible with standard manufacturing
techniques to facilitate wide use and experimentation by
many participants (i.e. universities, research labs, existing
fabs, etc.) without a significant investment in new equipment.
• Design should be easy to integrate with standard electronics
components and materials.
• Design should incorporate materials capable of RHIGH>>RLOW.
• Design should be robust to both temporal and spatial
variation of memristance.
• Design should avoid internal feedback current paths in
crossbar which can limit speed and ability to read resistance
states reliably.
• Design should allow ease of reconfiguration of resistance
states.

Nanowire vs. Microscale Wire Crossbars
• Some applications may not require nanowires
to provide competitive solutions in a variety
of areas (e.g. signal processing, pattern
recognition)
• Near‐term implementation is likely to be
easier and more readily adopted using
microscale wires which can avoid the
problems of nanowire defects, addressing,
etc.

Ideally, the detected resistance state of a selected crossbar
junction should be independent of other resistance states in the
crossbar.
1 0 0 0
Vread
1
0
0
Low resistance
junction
(i.e. logic 1)
High resistance
junction
(i.e. logic 0)

However, this is not the case for simple crossbar designs due to
internal currents.
1 0 0 0
Vread
1
0
0

A simple solution compatible with microfabrication
techniques is the incorporation of pn junctions in the
crossbar (taught by Ovshinsky for phase change crossbar
memory in US Patent 4,597,162). One variation of this
solution adapted to bilayer oxide memristive films is as
follows:
A) Film deposition of metal layer and silicon layers
n‐doped polysilicon
p‐doped polysilicon
metal layer (e.g. Al)
SiO2
Silicon wafer

B) Etch metal and semiconductor layers to form crossbar columns.
SiO2
Silicon wafer
N
P
N
P
N
P
N
P
Al Al Al Al

C) Deposit SiO2 in gaps to provide isolation and planarize surface
D) Deposit memristor oxide bilayer (e.g. TiO2/TiO2‐x)
E) Deposit and pattern top metal layer to form crossbar row wires
Pt
SiO2 SiO2 SiO2
SiO2
Silicon wafer
N
P
N
P
N
P
N
P
Al Al Al Al
TiO2/TiO2‐x

Desirable Goals for Memristor
Crossbar Array Applications
• Complement (not conflicting with) existing
technologies and markets to achieve ease of
acceptance
• Identify uses compatible with smaller emerging
markets with potential for high growth (e.g.
FPAAs , commercial robotics, neural interfaces)
• Solve problems for which conventional electronic
hardware and software do not provide efficient
solutions but which memristors can (e.g. pattern
recognition, traveling salesman problem)

Op‐amps are well known to be
implemented as summing amplifiers
R1
_
+
RF
Vout = - [V1(RF/R1)+V2(RF/R2)+
V3(RF/R3)+V4(RF/R4)]
R2
R3
R4
V1
V2
V3
V4

Integrating Memristor Crossbar Array with Op‐
Amp Circuitry = Matrix Summing Amplifier
M11 M12 M13 M14
M21 M22 M23 M24
M31 M32 M33 M34
M41 M42 M43 M44
R1 R2 R3 R4
V1 V2 V3 V4
_
+
RF
Vout

For an ideal op‐amp:
=−Σ
F ij I I
ij
V V
( −
)
Σ +
= −
i loss
ij ij i
V
out
F
M R
R
( )
V R − = − −
= −Σ Σ
out V V T M V V
( ) ( )( )
F
( ) i loss
ij
ij
ij
i loss
M +
R
ij i

To achieve behavior similar to binary logic requires
T(Mij=RHIGH)=0 and T(Mij=RLOW)=1
By setting the fixed column resistors Ri = RF‐RLOW then T(RLOW)=1
is achievable. However, T(RHIGH) is not able to be zero since this
would require RF = 0. Thus a low bit error (LBE) exists and a
tolerable LBE should satisfy:
T(RHIGH)= LBE ≥ 1/[1+(RHIGH‐RLOW)/RF]
Solving for RF produces:
RF ≤ [LBE/(1‐LBE)] (RHIGH‐RLOW)
This inequality sets an upper limit to RF based on a maximum
allowable low bit error and the high and low resistance values of
the memristor.

Analysis for memristor (Mij) variation
For an allowable bit sensitivity σ(Mij),
R dM
F ij
ij M
d T M <σ
| ( ) | 2 ij
( )
M R
( +
)
ij i
=
The maximum allowable value of d|T(Mij)| is at Mij = RLOW
and for Ri = RF‐RLOW
dM
R dM
F ij M
( )
( )2 ij
F
ij
LOW F LOW
R
R R R
= <σ
+ −

Combining the previous conditions the optimum range for RF can
be determined based on the memristance sensitivity and low bit
error as:
R LBE
dM
ij R R
( )
( ) 1 F HIGH LOW
ij
LBE
M
−
−
< <
σ

A possible rule of thumb is to set the allowable low bit error and bit
sensitivity in terms of the number of columns of the crossbar (=N).
For example, if all of the crosspoints have a low bit error and sensitivity
of 1/N a total of 1 bit error is produced at the output. The range of RF
may then be expressed as:
NdM R N −
ij F HIGH LOW R R
( )
1/
1 1/
N
−
< <

For large N the above inequality can be approximated to find the
maximum allowable variation in Mij as:
dM RHIGH RLOW
( )
2
N
ij
−
<
In case of relaxing the maximum bit error and sensitivity constraints
to allow for n total bit errors for N columns the above equation
becomes:
2 ( )
dM n RHIGH RLOW
2
N
ij
−
<

Potential Application #1
Programmable Drive Waveforms

Problems with Conventional Drive
Waveform Circuits
• In many electronics applications variation of circuit
parameters due to temperature change, aging, etc. require
adjustment of drive waveforms (e.g. LEDs may require a
higher amplitude voltage drive over time to produce a
consistent light output).
• Waveform adjustment is also desirable for mode adjustment
in various applications (e.g. inkjet printheads changing
resolution or drop size often involves timing or amplitude
adjustment of drive signal for heater or piezo.)
• Timing modulation and amplitude modulation circuits
implemented in hardware can require complex circuitry and
have limitations in adaptability and the range of possible
waveforms.
• Software based solutions require a microprocessor which can
be difficult/expensive to miniaturize for several portable
electronics applications

Memristor Crossbar Drive Waveform Circuit
Assuming RLOW of memristors << RF, R
1 0 0 0
_
+
RF
|Vout/Vin|≈
RF/R
R
2R
4R
8R
Voltage Level Converter
Shift Register

0 1 0 0
_
+
RF
|Vout/Vin|≈
RF/2R
R
2R
4R
8R
Shift Register

0 0 1 0
_
+
RF
|Vout/Vin|≈
RF/4R
R
2R
4R
8R
Shift Register

0 0 0 1
_
+
RF
|Vout/Vin|≈
RF/8R
R
2R
4R
8R
Shift Register

Advantages
• Both timing and amplitude of output waveform
can be adjusted by binary switching of the
memristance states in the crossbar.
• Even with only binary memristance switching, a
very large number of possible drive waveforms
are available (2NxM) (e.g. 10x10Æ1030 states).
• Combined with techniques such as hill climbing
and genetic algorithms has potential for self‐optimizing
drive waveforms and real‐time
adaption of circuitry to effects of aging and
temperature variation.

Pattern Recognition

Problems with Conventional Pattern
Recognition Solutions
• Software‐based solutions require time for data
transfer between memory and processor circuits
which causes a lag in responsiveness.
• Hardware solutions can be faster but have limits
in adaptability and limits in the range of patterns
that can be classified.
• Memristors offer a route to a “morphware”
pattern recognition solution combining both
memory storage and data processing in a
common circuit.

V1 V2 V3 V4 V5 V6 V7 V8
_
+
RF
Memristor Crossbar Pattern Comparison Circuit (write mode)
_
+
-Vref
1 0 1 0
1
0
0
0
Voltage Level Converter (V>Vthreshold)

V1 V2 V3 V4 V5 V6 V7 V8
_
+
RF
_
+
-Vref
0 1 1 1
0
1
0
0

V1 V2 V3 V4 V5 V6 V7 V8
_
+
RF
_
+
-Vref
1 1 0 0
0
0
1
0

V1 V2 V3 V4 V5 V6 V7 V8
_
+
RF
_
+
-Vref
0 0 1 0
0
0
0
1

V1 V2 V3 V4 V5 V6 V7 V8
_
+
RF
Memristor Crossbar Pattern Comparison Circuit (detect mode)
_
+
-Vref
0 1 1 1
1
I
0
0
0
Voltage Level Converter (V<Vthreshold)

V1 V2 V3 V4 V5 V6 V7 V8
_
+
RF
_
+
-Vref
0 1 1 1
0
4I
1
0
0

V1 V2 V3 V4 V5 V6 V7 V8
_
+
RF
_
+
-Vref
0 1 1 1
0
I
0
1
0

V1 V2 V3 V4 V5 V6 V7 V8
_
+
RF
_
+
-Vref
0 1 1 1
0
2I
0
0
1

Advantages
• Output voltage from 1st op‐amp is analogous to
XNOR (bit comparator) function
out ij V = A ΣT M V −V +ΣT M V −V −V
2 ([ ( )( i loss ) ( ij
)( i loss )] ref
) i
i
• Tuning Vref can adjust sensitivity of pattern
comparison and adjust allowable bit error
between resistance states and voltage states.
• Allowing for bit error could potentially be very
useful to applications such as facial recognition
which can require robustness to a large
percentage of bit errors.

FPAAs

Problems with Conventional FPAAs
• FPAAs (Field Programmable Analog Arrays)
provide reconfigurability of filter designs useful in
communications and control systems but are
limited in the range of configurable states.
• Lacks ability to electrically tune the resistance
such as provided by memristors to achieve
intermediate frequency states (for
communication apps.) or pole/zero adjustment
(for control apps.)

By including memristor crossbar junctions in the input and feedback path of
an op‐amp capacitor array, a transfer function can be tuned to adjust the
gain, pole, and/or zero of a filter.
Memristance programming circuitry
M21 M22 M23 M24 M25
M11 M12 M13 M14 M15 Vout(t)
C
R1 C/2 C/4 C/8
Vin(t)
R2 C C/2 C/4 C/8
Vout(s)/Vin(s) = K(Mij) [1+s/a(Mij)]/[(1+s/b(Mij)]
a(Mij)<<b(Mij) Æ high pass filter
a(Mij>>b(Mij) Æ low pass filter

Advantages
• Conversion between low pass and high pass
filter by appropriate selection of on/off states
of Mij.
• Tuning of f‐3dB or pole/zero by adjustment of
memristance state.
• Cascading multiple stages can provide for
tunable bandpass adjustment
• Dynamic PID controllers can be implemented
by connecting multiple stages in parallel.

Arithmetic Optimization Problems

Problems with Conventional Arithmetic
Processor Designs
• Segmentation between memory and
processor circuitry may produce a bottleneck
in speed. New clock independent designs are
desirable.
• Logic based arithmetic is inefficient for some
network optimization problems such as the
traveling salesman problem involving
repeated recalculation of sums

Memristor Crossbar Arithmetic Circuit
Assuming RLOW of memristors << R/8
0 1 0 1
_
+
R
Vout
R
R/2
R/4
R/8
20
21
22
23
Analog output representative of 2+4

Memristor Crossbar Arithmetic Circuit
Assuming RLOW of memristors << R/8
1 0 1 0
_
+
R
Vout
R
R/2
R/4
R/8
20
21
22
23
Analog output representative of 1+3

Advantages
• Although op‐amps are slower than logic circuits the
combination of memory and processing in a single
circuit reduces the reliance on a clock.
• May be scalable to provide real time solutions to
network optimization. For example, in a traveling
salesman problem with 100 nodes including 5050
inter‐relational distances, each distance metric can be
stored in a different crossbar column. Comparisons
between different paths between the nodes only
requires changing the bit pattern input to the crossbar
rows and detecting the analog level of voltage output.

Signal Mixing

Problems with Conventional Modulation
Systems
• Increase in portable wireless electronics
requires more efficient uses of spectrum with
techniques such as frequency hopping.
• Simpler but more secure signal encryption
methods are desirable.

Back‐to‐back diode memristor crossbar
Silicon wafer
SiO2
Al
N
P
Pt
TiO2/TiO2‐x Pt Pt Pt Pt
SiO2 SiO2 SiO2
SiO2
Silicon wafer
P
N
P
N
P
N
P
N
Al Al Al Al

Equivalent circuit at each crossbar junction
VeqB = VinB [(Ri+Mij)/(Ri+Rj+Mij)]+
VinA[Rj/(Ri+Rj+Mij)]
For VinB less than the diode
threshold voltage, the
resistance state of Mij and
voltage state of VinA can be
used to modulate signal
transmission.
VoutA
VinB
VoutB (ZB=∞)
VinA
Ri
Rj
RL
Mij
VeqB

Input Wiring
Vsig1
Vsig2
Vsig3
Vsig4
Vmod1 Vmod2 Vmod3 Vmod4

Advantages
• Switching of carrier frequencies allow more
efficient use of spectrum and compensation
for crowded channels.
• Potential exist for improved signal encryption
by sharing a randomized memristance
switching pattern between sender and
receiver.

Artificial Intelligence

Potential Routes to Strong A.I. with
Memristor Crossbars
• Neural Networks (feedforward of nonlinear
weighted sums of memristance states)
• Genetic Algorithms (selection, crossover, and
mutation of memristance states)
• Emergent Complexity from Memory‐
Prediction Framework (Hawkins approach)

Memristor Crossbar Circuit Design for
Sensor Stimulated Emergent Behavior
1st crossbar array
Differential
Amplifiers
Summing
Amplifiers
V11 V12 V13 V14 V21 V22 V23 V24
Vsen1 Vsen2 Vsen3 Vsen4
Vact1
Vact2
Vact3
Vact4
M11 M12 M13 M14
M21 M22 M23 M24
M31 M32 M33 M34
M41 M42 M43 M44
Analog Sensor Voltages
Analog
Actuator
Voltages

Memristor Crossbar Circuit Design for
Sensor Stimulated Emergent Behavior
2nd crossbar array
Differential
Amplifiers
Summing
Amplifiers
V21 V22 V23 V24 V11 V12 V13 V14
Vsen5 Vsen6 Vsen7 Vsen8
Vact5
Vact6
Vact7
Vact8
M11 M12 M13 M14
M21 M22 M23 M24
M31 M32 M33 M34
M41 M42 M43 M44
Analog Sensor Voltages
Analog
Actuator
Voltages
Recursive feedback
between 1st and 2nd
crossbar can potentially
set up a dynamic
memory‐prediction
framework for
intelligence

Potential Applications #7 and 8
Neural Interfaces (spike counting and
classification) and Robotics (enhanced
responsiveness for actuator control)
‐To be continued at
NSTI Nanotech Conference and Expo
May 3‐7, 2009
Houston, TX

Conclusion
• Thank you to organizers and participants.
• Thank you to Dr. Wei Wang of the College of
Nanoscale Science and Engineering for
information and feedback on FPAAs and
suggestion for error analysis.
• Thank you to Jeff Hawkins for insights on
memory‐prediction framework for intelligence
as documented in On Intelligence

Proposals for Memristor Crossbar Design and Applications

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