Carbon-based Electronics at LEM
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(2) ► Graphene and CNT high-frequency & flexible electronics. ► Programmable devices and circuits. January, 20 (2012) - vincent.derycke@cea.fr.
(3) High mobility materials for RF analog electronics. LG. P. Burke, U.C. Irvine (2004). January, 20 (2012) - vincent.derycke@cea.fr.
(4) HF Nanotube transistors. 1000 Projection (ballistic case and Lg=300 nm) Projection (diffusive case and Lg=300 nm). Frequency (GHz). 100 IEMN & CEA. 10. De-embedded. UIUC & NCC. 1. As measured. NEC. IBM (Single CNT). U.C. Irvine. 0.1 IBM (Single CNT ring oscillator). 0.01 2003 . . Bethoux et al, Elec. Dev. Lett. 27, 681 (2006) Le Louarn et al, Appl. Phys. Lett. 90, 233108 (2007) Nougaret et al, Appl. Phys. Lett. 94, 243505 (2009). 2004. 2005. 2006. 2007. 2008. 2009. 2010. Year. Collaboration. January, 20 (2012) - vincent.derycke@cea.fr.
(5) Graphene-based HF electronics 100 GHz → 240 GHz. 300 GHz. c. IBM: Lin et al, Science 327, 662 (2010).. d. UCLA: Liao et al, Nature (2010). HEMT InAs et InGaAs: fT > 600 GHz n-MOSFETs fT = 485 GHz p-MOSFET fT = 345 GHz January, 20 (2012) - vincent.derycke@cea.fr.
(6) Solution-based Graphene FETs. Impedance in the 100-200 Ω range adapted to HF measurements. B. 200 nm. C. D. 160. Pd. 150. Y2Ox. RDS (). A. 140. 130. VDS = -200 mV. 120 -4. -3. -2. -1. 0. 1. 2. 3. VGS (V). January, 20 (2012) - vincent.derycke@cea.fr.
(7) Solution-based Graphene FETs. S. Sij. 10 S11. H21 at 510 MHz (dB). D. Sij (dB). S G. A. S22. -20. PORT 2. PORT 1. 0. S12. -40. B. holes. electrons. 5. 0. -5. -60. From the S-parameters, we extract all the RF metrics for both holes and electrons. S21. -10. 0.1. 1. -1.5. 10. -0.5. 0.0. 0.5. 1.0. VGS (V). frequency (GHz). 0.5. 25. C. 20. holes. 0.4. -1.0. D. 0.3. H21 (dB). gm (mS). 15. 0.2. 10. f T (electrons)= 430 MHZ. 5. f T (holes)= 1.3 GHz. 0. electrons. 0.1. -5 0.0 0.1. 1 frequency (GHz). 10. -10 0.1. 1. 10. frequency (GHz). January, 20 (2012) - vincent.derycke@cea.fr.
(8) Solution-based Graphene FETs 30 After Joule annealing. 25 20 H21 , U (dB). 15 10 5 0 -5. fMAX = 550 MHz. -10 fT (ext) = 2.2 GHz. -15 -20. fT (int) = 8.7 GHz. -25 0.1. 1. 10. frequency (GHz). constant transconductance fT = 2.2 GHz for holes (before de-embedding) fT = 8.7 GHz for holes (after de-embedding). Competitive against solution-based organic FETs. fMAX = 550 MHz (better than flexible CNT-FETs). LC ~260 nm. LG ~170 nm. (no large-scale low-cost constraints considered yet). 200 nm. January, 20 (2012) - vincent.derycke@cea.fr.
(9) Flexible & HF Graphene FETs. 400. 400. 300. 300. fT_MAX (MHz). fT (MHz). G. Dambrine, H. Happy. 200 Flat (before bending) R= 71.5 mm R= 25 mm R= 12.5 mm Flat (after bending). 100. 0 0.0. Flat. 71.5. 25. 12.5. 200. 100. HF measurements upon bending. 0 0.2. 0.4. 0.6. VGS (V). 0.8. 1.0. 0.00. 0.02. 0.04. 0.06. 0.08 -1. Inverse bending radius (mm ). January, 20 (2012) - vincent.derycke@cea.fr.
(10) Solution-based 2D-materials FETs. January, 20 (2012) - vincent.derycke@cea.fr.
(11) ► Graphene and CNT high-frequency & flexible electronics. ► Programmable devices and circuits. January, 20 (2012) - vincent.derycke@cea.fr.
(12) Defects and device-to-device variability are expected to increase at the nano-scale. → Programmable circuits and circuit with function learning capabilities could handle large degree of variability. → Require memory devices. January, 20 (2012) - vincent.derycke@cea.fr.
(13) CNT-based circuits with learning capabilities Si Nanowire Local Gate. SWNT. Drain Si0 2 Si P+ Global Back Gate. Source 200 nm. Device physics Synapses (nanotubes). Neurones (silicium). Modeling and simulation. Circuit topology & learning rules. Simple prototype of mixed circuits. Collaboration J-O. Klein. Ch. Gamrat. X1+. X2+. X3+. F1: A.B.C. VG1. VG2. FPGA. Bias+. T2,2- T2,2+. C.(/A+/ C.(/A+/ B) B). MAJ(/A, MAJ(/A, B, B, C) C). F2: A.(B+C). F3: A+B+C. VG3. 00 00 00 00 11 11 11 00 X1-. X2-. X3-. 00 00 11 00 11 00 11 11. Bias-. NAND(A, NAND(A, B, B, C) C). /A.B.C /A.B.C. January, 20 11(2012) - vincent.derycke@cea.fr 11 11 11 11 11 11 00 00 00 00 00 00 00 11 00.
(14) Organic resistive-memory based circuits with learning capabilities. Matériau memristif Memristor. -4. 10. -5. I (A). 10. -6. 10. -7. 10. -8. 10. -3. -2. -1. 0. 1. 2. Circuit topology & learning rules. 3. V (V). Chemistry & Device physics. Crossbar integration Modeling and simulation. Collaboration J-O. Klein. Ch. Gamrat. B. Jousselme. Prototype of mixed circuits. Cointegration nano/CMOS. January, 20 (2012) - vincent.derycke@cea.fr.
(15) Organic resistive-memory devices with B. Jousselme. ERASE. 2 -4. V (V). 10. -5. 10. 400 0. WRITE. -2. READ. 200. I (A). -6. 10. -6. WRITE. ERASE. 0. -7. 10. -8. I (µA). READ ON. -4. READ OFF. -10 -8. 10. -200 -12. -3. -2. -1. 0. V (V). 1. 2. 3. 0. 50. 100. 150. 200. time (s). ► Non volatile resistive memory devices with manageable variability on programming thresholds. January, 20 (2012) - vincent.derycke@cea.fr.
(16) Organic resistive-memory devices Olivier Segut &. Bruno Jousselme. Transferred top electrodes Grafted bottom electrodes. ► Robust covalent polymer compatible with micro-contact printing. January, 20 (2012) - vincent.derycke@cea.fr.
(17) A real-life problem Real data from an AER Retina (Delbruck et al, available online). Olivier Bichler Damien Querlioz Christian Gamrat Simon Thorpe. 2 million devices with STDP After watching the freeway for 10 minutes, the system can count the cars on each lane with a 98% accuracy and high tolerance to variability in a fully unsupervised manner. Bichler et al, Int. Joint Conf Neur. Netw, 2011.
(18) Robustness to variability Simple benchmarking problem: handwritten digits recognition. Examples from the MNIST dataset (60 000 digits from 250 writers). Ingredients: - 28x28 pixel gray scale images - Color are coded as pulse train frequency - 1 memristor / pixel / neuron - STDP + inhibition Learning: all 60 000 digits presented 3 times Test: examples from 10 000 additional digits not used for training. Olivier Bichler Damien Querlioz Christian Gamrat Simon Thorpe.
(19) Robustness to variability Simple benchmarking problem: handwritten digits recognition. Olivier Bichler Damien Querlioz Christian Gamrat Simon Thorpe. Examples from the MNIST dataset (60 000 digits from 250 writers). Ingredients: - 28x28 pixel gray scale images - Color are coded as pulse train frequency - 1 memristor / pixel / neuron - STDP + inhibition. With 300 neurons x 784 synapses (235,200 memristors) 93.5% of digits are recognized (vs. 95% for traditional neural network with the same number of synapses), but through a fully unsupervised learning. Learning: all 60 000 digits presented 3 times Test: examples from 10 000 additional digits not used for training. D. Querlioz et al, Int. Joint Conf. Neur. Net. (2011).
(20) Robustness to variability Olivier Bichler Damien Querlioz Christian Gamrat Simon Thorpe. Simple benchmarking problem: handwritten digits recognition. With 39200 synapses (50 neurons). Dispersion on the parameter. 10%. 25%. 50%. 100%. Initial weight. 81.3%. 82.0%. 80.8%. 81.3%. Learning increments and decrements. 81.8%. 81.4%. 79.0%. 74.0%. Learning increments and decrements, min. and max. resistance. 81.9%. 80.6%. 77.2%. 67.8%. No variability: 82.0% of recognition rate. The initial state does no need to be controlled at all. At 50% variability, 4% of the devices cannot be programmed at all -> still ~77% recognition rate This scheme can tolerate extreme levels of synaptic variability D. Querlioz et al, Int. Joint Conf. Neur. Net. (2011).
(21) ► Graphene and CNT high-frequency & flexible electronics 1. Graphene HF and printed electronics 2. 2D-semiconductors. ► Programmable devices and circuits 1. µCP -> nano-contact printing 2. Mixed silicon-organic circuits. 3. Learning rules 4. Simulation -> demonstrators January, 20 (2012) - vincent.derycke@cea.fr.
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