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SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations


Although several types of architecture combining memory cells and transistors have been used to demonstrate artificial synaptic arrays, they usually present limited scalability and high power consumption. Transistor-free analog switching devices may overcome these limitations, yet the typical switching process they rely on—formation of filaments in an amorphous medium—is not easily controlled and hence hampers the spatial and temporal reproducibility of the performance. Here, we demonstrate analog resistive switching devices that possess desired characteristics for neuromorphic computing networks with minimal performance variations using a single-crystalline SiGe layer epitaxially grown on Si as a switching medium. Such epitaxial random access memories utilize threading dislocations in SiGe to confine metal filaments in a defined, one-dimensional channel. This confinement results in drastically enhanced switching uniformity and long retention/high endurance with a high analog on/off ratio. Simulations using the MNIST handwritten recognition data set prove that epitaxial random access memories can operate with an online learning accuracy of 95.1%.

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Fig. 1: Impact of dislocation on the characteristics of the SiGe epiRAM.
Fig. 2: The characteristics of the SiGe epiRAM after widening the dislocation pipes with a dislocation-selective etch.
Fig. 3: The characteristics of epiRAM for neuromorphic computing.
Fig. 4: The image recognition simulation.


  1. Jo, S. H. et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10, 1297–1301 (2010).

    Article  Google Scholar 

  2. Burgt, Y. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017).

    Article  Google Scholar 

  3. Burr, G. W. et al. Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element. In 2014 IEEE Int. Electron Devices Meeting 29.5.1-29.5.4 (IEEE, 2014).

  4. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2016).

  5. Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015).

    Article  Google Scholar 

  6. Lee, M.-J. et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5−x/TaO2−x bilayer structures. Nat. Mater. 10, 625–630 (2011).

  7. Gokmen, T. & Vlasov, Y. Acceleration of deep neural network training with resistive cross-point devices: Design considerations. Front. Neurosci. 10, 333 (2016).

  8. Shibuya, K., Dittmann, R., Mi, S. & Waser, R. Impact of defect distribution on resistive switching characteristics of Sr2TiO4 thin films. Adv. Mater. 22, 411–414 (2010).

    Article  Google Scholar 

  9. Park, G.-S. et al. In situ observation of filamentary conducting channels in an asymmetric Ta2O5−x/TaO2−x bilayer structure. Nat. Commun. 4, 495707 (2013).

    Google Scholar 

  10. Yu, S., Guan, X. & Wong, H.-S. P. Conduction mechanism of TiN∕HfO x ∕Pt resistive switching memory: A trap-assisted-tunneling model. Appl. Phys. Lett. 99, 063507 (2011).

    Article  Google Scholar 

  11. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).

    Article  Google Scholar 

  12. Szot, K., Speier, W., Bihlmayer, G. & Waser, R. Switching the electrical resistance of individual dislocations in single-crystalline SrTiO3. Nat. Mater. 5, 312–320 (2006).

    Article  Google Scholar 

  13. Kim, K.-H. et al. A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. Nano Lett. 12, 389–395 (2012).

    Article  Google Scholar 

  14. Yang, Y. et al. Electrochemical dynamics of nanoscale metallic inclusions in dielectrics. Nat. Commun. 5, 377–383 (2014).

    Google Scholar 

  15. Jo, S. H., Kim, K. H. & Lu, W. High-density crossbar arrays based on a Si memristive system. Nano Lett. 9, 870–874 (2009).

    Article  Google Scholar 

  16. Jo, S. H. & Lu, W. CMOS compatible nanoscale nonvolatile resistance switching memory. Nano Lett. 8, 392–397 (2008).

    Article  Google Scholar 

  17. Waser, R., Dittmann, R., Staikov, G. & Szot, K. Redox-based resistive switching memories - nanoionic mechanisms, prospects, and challenges. Adv. Mater. 21, 2632–2663 (2009).

    Article  Google Scholar 

  18. Yang, Y. et al. Observation of conducting filament growth in nanoscale resistive memories. Nat. Commun. 3, 732 (2012).

    Article  Google Scholar 

  19. Ielmini, D. & Waser, R. Resistive Switching: From Fundamentals of Nanoionic Redox Processes to Memristive Device Applications (Wiley-VCH, Weinheim, Germany, 2016).

  20. Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).

    Article  Google Scholar 

  21. Krishnan, K., Tsuruoka, T., Mannequin, C. & Aono, M. Mechanism for conducting filament growth in self-assembled polymer thin films for redox-based atomic switches. Adv. Mater. 28, 640–648 (2016).

    Article  Google Scholar 

  22. Alibart, F., Zamanidoost, E. & Strukov, D. B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat. Commun. 4, 2072 (2013).

    Article  Google Scholar 

  23. Speck, J. S., Brewer, M. A., Beltz, G., Romanov, A. E. & Pompe, W. Scaling laws for the reduction of threading dislocation densities in homogeneous buffer layers. J. Appl. Phys. 80, 3808 (1996).

    Article  Google Scholar 

  24. Porter, D. A., Easterling, K. E. & Sherif, M. Y. Phase Transformations in Metals and Alloys (CRC Press, Boca Raton, USA, 2009).

  25. Houghton, D. C. Strain relaxation kinetics in Si1-xGe x /Si heterostructures. J. Appl. Phys. 70, 2136–2151 (1991).

    Article  Google Scholar 

  26. Romanov, A. E., Pompe, W., Beltz, G. & Speck, J. S. Modeling of threading dislocation density reduction in heteroepitaxial layers I. Geometry and crystallography. Phys. Status Solidi 198, 599–613 (1996).

    Article  Google Scholar 

  27. Rollert, F., Stolwijk, N. A. & Mehrer, H. Solubility, diffusion and thermodynamic properties of silver in silicon. J. Phys. D 20, 1148 (1987).

    Article  Google Scholar 

  28. Effenberg, G., Aldinger, F. & Prince, A. Ternary Alloys 211–221 (VCH, Weinheim, Germany, 1988).

  29. Al-Joubori, A. A. & Suryanarayana, C. Synthesis of metastable NiGe2 by mechanical alloying. Mater. Des. 87, 520–526 (2015).

    Article  Google Scholar 

  30. Yu, S. et al. Scaling-up resistive synaptic arrays for neuro-inspired architecture: Challenges and prospect. In 2015 IEEE International Electron Devices Meeting (IEDM) 17.3.1–17.3.4 (INSPEC, London, 2015).

  31. Hull, R. Properties of Crystalline Silicon (Institution of Electrical Engineers, 2006).

  32. Wells, A. F. Structural Inorganic Chemistry (Oxford University Press, New York, USA, 2012).

    Google Scholar 

  33. Schimmel, D. G. Defect etch for <100> silicon evaluation. J. Electrochem. Soc. 126, 479–483 (1979).

    Article  Google Scholar 

  34. Chen, P.-Y., Gao, L. & Yu, S. Design of resistive synaptic array for implementing on-chip sparse learning. IEEE Trans. Multi-Scale Comput. Syst. 2, 257–264 (2016).

    Article  Google Scholar 

  35. Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).

    Article  Google Scholar 

  36. Lee, J., Du, C., Sun, K., Kioupakis, E. & Lu, W. D. Tuning ionic transport in memristive devices by graphene with engineered nanopores. ACS Nano 10, 3571–3579 (2016).

    Article  Google Scholar 

  37. You, B. K., Byun, M., Kim, S. & Lee, K. J. Self-structured conductive filament nanoheater for chalcogenide phase transition. ACS Nano 9, 6587–6594 (2015).

    Article  Google Scholar 

  38. Liu, Q. et al. Improvement of resistive switching properties in ZrO2-based ReRAM with implanted Ti ions. IEEE Electron Device Lett. 30, 1335–1337 (2009).

    Article  Google Scholar 

  39. Chang, W. Y., Lin, C. A., He, J. H. & Wu, T. B. Resistive switching behaviors of ZnO nanorod layers. Appl. Phys. Lett. 96, 242109 (2010).

    Article  Google Scholar 

  40. Yoon, J. H. et al. Highly improved uniformity in the resistive switching parameters of TiO2 thin films by inserting Ru nanodots. Adv. Mater. 25, 1987–1992 (2013).

    Article  Google Scholar 

  41. Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R. & Bengio, Y. Quantized neural networks: training neural networks with low precision weights and activations. Preprint at (2016).

  42. Ambrogio, S., Balatti, S., Choi, S. & Ielmini, D. Impact of the mechanical stress on switching characteristics of electrochemical resistive memory. Adv. Mater. 26, 3885–3892 (2014).

    Article  Google Scholar 

  43. LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2323 (1998).

    Article  Google Scholar 

  44. Kataeva, I., Merrikh-Bayat, F., Zamanidoost, E. & Strukov, D. Efficient training algorithms for neural networks based on memristive crossbar circuits. In Proc. Int. Joint Conf. Neural Networks 1–8 (IEEE, 2015).

  45. Chen, P.-Y., Peng, X.C. & Yu. S. User Manual of MLP Simulator (+NeuroSim) (accessed 1 January 2017);

  46. Chen, P.-Y., Peng, X. & Yu, S. NeuroSim+: An integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures. IEEE Int. Electron Devices Meeting (IEDM) (IEEE, San Francisco, USA, 2017).

  47. Prezioso, M. et al. Modeling and implementation of firing-rate neuromorphic-network classifiers with bilayer Pt/Al2O3/TiO2–x/Pt memristors. In Technical Digest - International Electron Devices Meeting, IEDM, 17.4.1–17.4.4 (IEEE, 2016).

  48. Ortiz-Conde, A. et al. A review of recent MOSFET threshold voltage extraction methods. Microelectron. Reliab. 42, 583–596 (2002).

    Article  Google Scholar 

  49. Gao, L. et al. Fully parallel write/read in resistive synaptic array for accelerating on-chip learning. Nanotechnology 26, 455204 (2015).

    Article  Google Scholar 

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This work is partially supported by NSF-SRC-E2CDA under contract no. 2018-NC-2762B. We thank J. J. Yang, Q. Xia, P. Lin, Y. Li, M. Rao and Y. Zhuo of the University of Massachusetts for valuable help and fruitful discussion. We also thank S. Kim of the IBM T.J. Watson Research Center for valuable suggestions for experiments. This work was performed in part at the Micro Technology Laboratories (MTL) at the Massachusetts Institute of Technology, and in part at the Harvard University Center for Nanoscale Systems (CNS), supported by the National Science Foundation under NSF ECCS award no. 1541959.

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S.C. and J.K. conceived this work and J.K. directed the team. S.C., S.H.T. and J.K. designed experiments. S.C., S.H.T., Z.L. and J.K. prepared the manuscript. S.H.T. and Y.K. carried out the epitaxial growth experiments and characterization. S.C., S.H.T., C.C. and H.Y. performed the device fabrication and electrical measurements of epiRAM devices and TEM/SEM characterization. Z.L., P.-Y.C. and S.Y. performed the simulation work. All authors discussed and contributed to the discussion and analysis of the results regarding the manuscript at all stages.

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Correspondence to Jeehwan Kim.

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Choi, S., Tan, S.H., Li, Z. et al. SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations. Nature Mater 17, 335–340 (2018).

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