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Probabilistic modelling of performance parameters of Carbon Nanotube transistors

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Probabilistic modelling of performance parameters of Carbon Nanotube transistors. Department of Electrical and Computer Engineering. By Yaman Sangar Amitesh Narayan Snehal Mhatre. Overview. Motivation Introduction CMOS v/s CNTFETs CNT Technology - Challenges - PowerPoint PPT Presentation
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Probabilistic modelling of performance parameters of Carbon Nanotube transistors Department of Electrical and Computer Engineering By Yaman Sangar Amitesh Narayan Snehal Mhatre
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Page 1: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Probabilistic modelling of performance parameters of Carbon Nanotube transistors

Department of Electrical and Computer Engineering

ByYaman Sangar

Amitesh NarayanSnehal Mhatre

Page 2: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Overview■ Motivation■ Introduction■ CMOS v/s CNTFETs■ CNT Technology - Challenges■ Probabilistic model of faults■ Modelling performance parameters:◻ION / IOFF tuning ratio

◻Gate delay◻Noise Margin

■ Conclusion

04/29/2014 1

Page 3: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Overview Motivation Introduction CMOS v/s CNTFETs CNT Technology – Challenges Probabilistic model of faults Modelling performance parameters:

ION / IOFF tuning ratio Gate delay Noise Margin

Conclusion

04/29/2014 2

Page 4: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

MOTIVATION: Why CNTFET?

04/29/2014 3

■ Dennard Scaling might not last long■ Increased performance by better algorithms?■ More parallelism?■ Alternatives to CMOS - FinFETs, Ge-nanowire FET, Si-

nanowire FET, wrap-around gate MOS, graphene ribbon FET ■ What about an inherently faster and less power consuming

device?■ Yay CNTFET – faster with low power

Page 5: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Overview Motivation Introduction CMOS v/s CNTFETs CNT Technology – Challenges Probabilistic model of faults Modelling performance parameters:

ION / IOFF tuning ratio Gate delay Noise Margin

Conclusion

04/29/2014 4

Page 6: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

5

Carbon Nanotubes

04/29/2014

■ CNT is a tubular form of carbon with diameter as small as 1nm

■ CNT is configurationally equivalent to a 2-D graphene sheet rolled into a tube.

Page 7: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

6

Types of CNTs

04/29/2014

■ Single Walled CNT (SWNT)■ Double Walled CNT (DWNT)■ Multiple Walled CNT (MWNT)

■ Depending on Chiral angle:• Semiconducting CNT (s-CNT)• Metallic CNT (m-CNT)

Page 8: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

7

Properties of CNTs

04/29/2014

■ Strong and very flexible molecular material■ Electrical conductivity is 6 times that of copper■ High current carrying capacity■ Thermal conductivity is 15 times more than copper■ Toxicity?

Page 9: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

04/29/2014 8

CNTFET

How CNTs conduct?

■ Gate used to electrostatically induce carriers into tube■ Ballistic Transport

Page 10: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Overview Motivation Introduction CMOS v/s CNTFETs CNT Technology – Challenges Probabilistic model of faults Modelling performance parameters:

ION / IOFF tuning ratio Gate delay Noise Margin

Conclusion

04/29/2014 9

Page 11: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Circuit FET Delay (In Picoseconds)

Power (In uWatts)

Inverter CMOS 16.58 9.81

CNT 3.78 0.25

2 Input Nand CMOS 24.32 20.67

CNT 5.98 0.69

2 Input Nor CMOS 39.26 22.13

CNT 6.49 0.48

Simulation based Comparison between CMOS and CNT technology

04/29/2014 10

Page 12: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

04/29/2014 11Better delay

Circuit FET Delay (In Picoseconds)

Power (In uWatts)

Inverter CMOS 16.58 9.81

CNT 3.78 0.25

2 Input Nand CMOS 24.32 20.67

CNT 5.98 0.69

2 Input Nor CMOS 39.26 22.13

CNT 6.49 0.48

Simulation based Comparison between CMOS and CNT technology

Page 13: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

04/29/2014 12Better delay

At lower power!

Circuit FET Delay (In Picoseconds)

Power (In uWatts)

Inverter CMOS 16.58 9.81

CNT 3.78 0.25

2 Input Nand CMOS 24.32 20.67

CNT 5.98 0.69

2 Input Nor CMOS 39.26 22.13

CNT 6.49 0.48

Simulation based Comparison between CMOS and CNT technology

Page 14: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Overview Motivation Introduction CMOS v/s CNTFETs CNT Technology – Challenges Probabilistic model of faults Modelling performance parameters:

ION / IOFF tuning ratio Gate delay Noise Margin

Conclusion

04/29/2014 13

Page 15: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

■ Major CNT specific variations■ CNT density variation■ Metallic CNT induced count variation■ CNT diameter variation■ CNT misalignment■ CNT doping variation

Challenges with CNT technology

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■ Unavoidable process variations

■ Performance parameters affected

Page 16: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

CNT density variation CNT diameter variation

■ Current variation

■ Threshold voltage variation

04/29/2014 15

Page 17: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

CNT Misalignment CNT doping variation

■ Changes effective CNT length■ Short between CNTs■ Incorrect logic functionality■ Reduction in drive current

■ May not lead to unipolar behavior

04/29/2014 16

Page 18: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Metallic CNT induced count variation

m-CNTs-CNT

■ Excessive leakage current■ Increases power consumption■ Changes gate delay■ Inferior noise performance■ Defective functionality

s-CNT

m-CNT

Vgs

Cur

rent

04/29/2014 17

Page 19: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

18

Removal of m-CNTFETs

■ VMR Technique : A special layout called VMR structure consisting of inter-digitated electrodes at minimum metal pitch is fabricated. M-CNT electrical breakdown performed by applying high voltage all at once using VMR. M-CNTs are burnt out and unwanted sections of VMR are later removed.

■ Using Thermal and Fluidic Process: Preferential thermal desorption of the alkyls from the semiconducting nanotubes and further dissolution of m-CNTs in chloroform.

■ Chemical Etching: Diameter dependent etching technique which removes all m-CNTs below a cutoff diameter.

04/29/2014

Page 20: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Overview Motivation Introduction CMOS v/s CNTFETs CNT Technology – Challenges Probabilistic model of faults Modelling performance parameters:

ION / IOFF tuning ratio Gate delay Noise Margin

Conclusion

04/29/2014 19

Page 21: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Probabilistic model of CNT count variation due to m-CNTs

■ ps = probability of s-CNT■ pm = probability of m-CNT■ ps = 1 - pm

■ Ngs = number of grown s-CNTs■ Ngm = number of grown m-CNTs■ N = total number of CNTs

Probability of grown CNT count

04/29/2014 20

Page 22: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Conditional probability after removal techniques

■ Ns = number of surviving s-CNTs■ Nm = number of surving m-CNTs■ prs = conditional probability that a CNT is removed given that it is s-CNT ■ prm = conditional probability that a CNT is removed given that it is m-CNT■ qrs = 1 - prs

■ qrm = 1 -prm

04/29/2014 21

P൛Ns = ns|Ngs = ngsൟ= ngsCnsqrsnsprs

(ngs-ns) P൛Nm = nm|Ngm = ngmൟ= ngmCnmqrm

(ngm-nm)prmnm

Page 23: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Overview Motivation Introduction CMOS v/s CNTFETs CNT Technology – Challenges Probabilistic model of faults Modelling performance parameters:

ION / IOFF tuning ratio Gate delay Noise Margin

Conclusion

04/29/2014 22

Page 24: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Effect of CNT count variation on ION / IOFF tuning ratio

■ ION / IOFF is indicator of transistor leakage

■ Improper ION / IOFF → slow output transition or low output swing

■ Target value of ION / IOFF = 104

04/29/2014 23

Page 25: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Current of a single CNT

ICNT = ps Is + pmIm

µ(ICNT) = psµ( Is )+ pmµ(Im )

■ ICNT = drive current of single CNT (type unknown)■ Is = drive current of single s-CNT■ Im = drive current of single m-CNT

■ ps = probability of s-CNT■ pm = probability of m-CNT

04/29/2014 24

Page 26: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

■ Ns = count of s-CNT■ Nm = count of m-CNT

■ Is,on = s-CNT current, Vgs = Vds = Vdd

■ Is,off = s-CNT current, Vgs = 0 and Vds = Vdd

■ Im = m-CNT current, Vds = Vdd

ION / IOFF ratio of CNTFET

04/29/2014 25

Page 27: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

µ (Ns) = ps (1 - prs) N

µ (Nm) = pm (1 - prm) N

ION / IOFF ratio of CNTFET

04/29/2014 26

Page 28: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Effect of various processing parameters on the ratio µ(ION) / µ(IOFF)

■ µ(ION) / µ(IOFF) is more sensitive to prm

■ µ(ION) / µ(IOFF) = 104 for prm > 1 – 10 -4 = 99.99 % for pm = 33.33%

04/29/2014 27

1- prm

𝜇 (𝐼 𝑜𝑛)𝜇(𝐼 𝑜𝑓𝑓 )

Page 29: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Overview Motivation Introduction CMOS v/s CNTFETs CNT Technology – Challenges Probabilistic model of faults Modelling performance parameters:

ION / IOFF tuning ratio Gate delay Noise Margin

Conclusion

04/29/2014 28

Page 30: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

2904/29/2014

Effect of CNT count variation on Gate delay

delay=C load ∆V

I drive

Page 31: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

3004/29/2014

= =

σ (delay )≈ μ (delay)σ ( I drive)

¿¿σ (delay )≈

Cload V dd

μ2¿¿

μ(delay)≈C load V dd

¿¿

¿𝑝𝑠 σ 2 ( 𝐼 𝑠 )+𝑝𝑚 σ 2 ( 𝐼𝑚 )+𝑝𝑚𝑝𝑠 [ μ ( 𝐼 𝑠 )− μ ( 𝐼𝑚 ) ] 2

Page 32: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

31

Plot of v/s

04/29/2014

= 0.3σ (delay )μ(delay )

𝜎 𝑠

𝜇𝑠

N = 10

N = 20

N = 30N = 40

N = 50

Page 33: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

3204/29/2014

Plot of v/s N

σ (delay )μ(delay )

N

0.2 0.4 0.60.8

0.9

Page 34: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Overview Motivation Introduction CMOS v/s CNTFETs CNT Technology – Challenges Probabilistic model of faults Modelling performance parameters:

ION / IOFF tuning ratio Gate delay Noise Margin

Conclusion

04/29/2014 33

Page 35: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

34

Noise Margin of CNTFET

04/29/2014

Page 36: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

35

VIL and VIH

■ Substituting = Vin, , and

■ =

■ Differentiating with respect to Vin and substituting -1

04/29/2014

nFET

pFET

Page 37: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

04/29/2014 36

For CNTFET, For CMOS,

VIL and VIH

NML = VIL - 0

NMH = VDD – VIH

Page 38: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

Overview Motivation Introduction CMOS v/s CNTFETs CNT Technology – Challenges Probabilistic model of faults Modelling performance parameters:

ION / IOFF tuning ratio Gate delay Noise Margin

Conclusion

04/29/2014 37

Page 39: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

38

CONCLUSION

■ Modeled count variations and hence device current as a probabilistic function

■ Studied the affect of these faults on tuning ratio and gate delay■ Inferred some design guidelines that could be used to judge the correctness

of a process■ Mathematically derived noise margin based on current equations – better

noise margin than a CMOS

04/29/2014

Page 40: Probabilistic modelling of performance parameters of Carbon Nanotube  transistors

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