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Linear symmetric self-selecting 14-bit kinetic molecular memristors

  • Roy, K., Jaiswal, A. & Panda, P. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • AI hardware has an energy problem. Nat. Electron. 6, 463 (2023).

  • Xiao, T. P., Bennett, C. H., Feinberg, B., Agarwal, S. & Marinella, M. J. Analog architectures for neural network acceleration based on non-volatile memory. Appl. Phys. Rev. 7, 031301 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Woo, J. & Yu, S. Resistive memory-based analog synapse: The pursuit for linear and symmetric weight update. IEEE Nanotechnol. Mag. 12, 36–44 (2018).

    Article 

    Google Scholar
     

  • Hinton, G. The Forward-Forward algorithm: some preliminary investigations. Preprint at https://arxiv.org/abs/2212.13345 (2022).

  • Mehonic, A. & Kenyon, A. J. Brain-inspired computing needs a master plan. Nature 604, 255–260 (2022).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Fawzi, A. et al. Discovering faster matrix multiplication algorithms with reinforcement learning. Nature 610, 47–53 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Brown, T. et al. Language models are few-shot learners. In Proc. Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020 (eds Larochelle, H. et al.) Vol. 33, 1877–1901 (2020).

  • Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Zhao, H. et al. Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis. Nat. Commun. 14, 2276 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rao, M. et al. Thousands of conductance levels in memristors integrated on CMOS. Nature 615, 823–829 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Williams, R. S. What’s Next? [The end of Moore’s law]. Comput. Sci. Eng. 19, 7–13 (2017).

    Article 

    Google Scholar
     

  • Schuman, C. D. et al. Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2, 10–19 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Nussbaumer, H. J. in Fast Fourier Transform and Convolution Algorithms Vol. 2, 80–111 (Springer, 1982).

  • Chen, S., Zhang, T., Tappertzhofen, S., Yang, Y. & Valov, I. Electrochemical‐memristor‐based artificial neurons and synapses—fundamentals, applications, and challenges. Adv. Mater. 35, 2301924 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Li, Y. et al. Memristive field‐programmable analog arrays for analog computing. Adv. Mater. 35, 2206648 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Zhang, Y. et al. Brain-inspired computing with memristors: Challenges in devices, circuits, and systems. Appl. Phys. Rev. 7, 011308 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Merced-Grafals, E. J., Dávila, N., Ge, N., Williams, R. S. & Strachan, J. P. Repeatable, accurate, and high speed multi-level programming of memristor 1T1R arrays for power efficient analog computing applications. Nanotechnology 27, 365202 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Choi, S., Yang, Y. & Lu, W. Random telegraph noise and resistance switching analysis of oxide based resistive memory. Nanoscale 6, 400–404 (2014).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • van de 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 
    ADS 
    PubMed 

    Google Scholar
     

  • Korkmaz, A. et al. Memristor-based offset cancellation technique in analog crossbars. In 2023 IEEE International Symposium on Circuits and Systems (ISCAS) (IEEE, 2023).

  • He, C., Korkmaz, A., Katehi, L. P., Williams, R. S. & Palermo, S. Analog signal processing in high frequency circuits using crossbar configurations. In Proc. 2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS) 116–121 (IEEE, 2021).

  • Marinella, M. J. et al. Multiscale co-design analysis of energy, latency, area, and accuracy of a ReRAM analog neural training accelerator. IEEE J. Emerg. Sel. Top. Circuits Syst. 8, 86–101 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Rath, S. P., Thompson, D., Goswami, S. & Goswami, S. Many-body molecular interactions in a memristor. Adv. Mater. 35, 2204551 (2022).

    Article 

    Google Scholar
     

  • Goswami, S. et al. Charge disproportionate molecular redox for discrete memristive and memcapacitive switching. Nat. Nanotechnol. 15, 380–389 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Zoppo, G. et al. A mathematical formulation of the wire resistance problem in memristor crossbars. In Proc. 2022 IEEE 22nd International Conference on Nanotechnology (NANO) 461–464 (IEEE, 2022).

  • Jeong, Y., Zidan, M. A. & Lu, W. D. Parasitic effect analysis in memristor-array-based neuromorphic systems. IEEE Trans. Nanotechnol. 17, 184–193 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Liao, Y. et al. Diagonal matrix regression layer: Training neural networks on resistive crossbars with interconnect resistance effect. IEEE Trans. Comput. Aided Design Integr. Circuits Syst. 40, 1662–1671 (2020).

    Article 

    Google Scholar
     

  • Zoppo, G. et al. Analog solutions of discrete Markov chains via memristor crossbars. IEEE Trans. Circuits Syst. I Regul. Pap. 68, 4910–4923 (2021).

    Article 

    Google Scholar
     

  • Song, S., Miller, K. D. & Abbott, L. F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Shulz, D. & Feldman, D. in Comprehensive Developmental Neuroscience: Neural Circuit Development and Function in the Healthy and Diseased Brain. Ch. 9, 155–181 (Elsevier, 2013).

  • Goswami, S., Goswami, S. & Venkatesan, T. An organic approach to low energy memory and brain inspired electronics. Appl. Phys. Rev. 7, 021303 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Goswami, S. et al. Robust resistive memory devices using solution-processable metal-coordinated azo aromatics. Nat. Mater. 16, 1216–1224 (2017).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Goswami, S. et al. Decision trees within a molecular memristor. Nature 597, 51–56 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hänggi, P., Talkner, P. & Borkovec, M. Reaction-rate theory: fifty years after Kramers. Rev. Mod. Phys. 62, 251–341 (1990).

    Article 
    ADS 
    MathSciNet 

    Google Scholar
     

  • Goswami, S. et al. Nanometer‐scale uniform conductance switching in molecular memristors. Adv. Mater. 32, 2004370 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Yi, W. et al. Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors. Nat. Commun. 7, 11142 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Boyn, S. et al. Learning through ferroelectric domain dynamics in solid-state synapses. Nat. Commun. 8, 14736 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jo, J. et al. Domain switching kinetics in disordered ferroelectric thin films. Phys. Rev. Lett. 99, 267602 (2007).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Migliore, A. & Nitzan, A. Nonlinear charge transport in redox molecular junctions: a Marcus perspective. ACS Nano 5, 6669–6685 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Schwarz, F. et al. Field-induced conductance switching by charge-state alternation in organometallic single-molecule junctions. Nat. Nanotechnol. 11, 170–176 (2016).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Liang, G., Ghosh, A., Paulsson, M. & Datta, S. Electrostatic potential profiles of molecular conductors. Phys. Rev. B 69, 115302 (2004).

    Article 
    ADS 

    Google Scholar
     

  • Yuan, L. et al. Controlling the direction of rectification in a molecular diode. Nat. Commun. 6, 6324 (2015).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Lee, H. D. et al. Integration of 4F2 selector-less crossbar array 2Mb ReRAM based on transition metal oxides for high density memory applications. In Proc. 2012 Symposium on VLSI Technology (VLSIT) 151–152 (IEEE, 2012).

  • Choi, B. J. et al. Trilayer tunnel selectors for memristor memory cells. Adv. Mater. 28, 356–362 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Korkmaz, A. et al. Spectral ranking in complex networks using memristor crossbars. IEEE J. Emer. Sel. Top. Circuits Syst. 13, 357–370 (2023).

    Article 
    ADS 

    Google Scholar
     

  • Strachan, J. P., Hu, M., Williams, R. S. & Li, Z. Memristor crossbar array for performing a Fourier transformation. US Patent No. 10,621,267 (2020).

  • Kumar, A. A. & Makur, A. Hermitian symmetric DFT codes: a new class of complex DFT codes. IEEE Trans. Signal Process. 60, 2396–2407 (2012).

    Article 
    ADS 
    MathSciNet 

    Google Scholar
     

  • Jouppi, N. P. et al. In-datacenter performance analysis of a tensor processing unit. In Proc. 44th Annual International Symposium on Computer Architecture 1–12 (ACM, 2017).

  • Goswami, S., Thompson, D., Williams, R. S., Goswami, S. & Venkatesan, T. Colossal current and voltage tunability in an organic memristor via electrode engineering. Appl. Mater. Today 19, 100626 (2020).

    Article 

    Google Scholar
     

  • Tsioutsios, I. et al. Free-standing silicon shadow masks for transmon qubit fabrication. AIP Adv. 10, 065120 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Aksu, S. et al. High-throughput nanofabrication of infrared plasmonic nanoantenna arrays for vibrational nanospectroscopy. Nano Lett. 10, 2511–2518 (2010).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Yi, S. I. et al. Energy and space efficient parallel adder using molecular memristors. Adv. Mater. 35, 2206128 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Matsui, H., Takeda, Y. & Tokito, S. Flexible and printed organic transistors: From materials to integrated circuits. Org. Electron. 75, 105432 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Berggren, M. et al. Browsing the real world using organic electronics, Si‐chips, and a human touch. Adv. Mater. 28, 1911–1916 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gergel-Hackett, N., Zangmeister, C. D., Hacker, C. A., Richter, L. J. & Richter, C. A. Demonstration of molecular assembly on Si (100) for CMOS-compatible molecule-based electronic devices. J. Am. Chem. Soc. 130, 4259–4261 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gergel-Hackett, N., Hill, A. A., Hacker, C. A. & Richter, C. A. The integration of molecular electronic devices with traditional CMOS technologies. In Proc. 2008 8th IEEE Conference on Nanotechnology. 522–525 (IEEE, 2008).

  • Orji, N. G. et al. Metrology for the next generation of semiconductor devices. Nat. Electron. 1, 532–547 (2018).

    Article 

    Google Scholar
     

  • Xia, Q. et al. Memristor−CMOS hybrid integrated circuits for reconfigurable logic. Nano Lett. 9, 3640–3645 (2009).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Morris, T. W. et al. Multi‐electron reduction capacity and multiple binding pockets in metal–organic redox assembly at surfaces. Chem. A Eur. J. 25, 5565–5573 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Skomski, D., Abb, S. & Tait, S. L. Robust surface nano-architecture by alkali–carboxylate ionic bonding. J. Am. Chem. Soc. 134, 14165–14171 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gibney, E. The inside story on wearable electronics. Nature 528, 26–28 (2015).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Xu, J. et al. Highly stretchable polymer semiconductor films through the nanoconfinement effect. Science 355, 59–64 (2017).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Ates, H. C., Yetisen, A. K., Güder, F. & Dincer, C. Wearable devices for the detection of COVID-19. Nat. Electron. 4, 13–14 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Park, S. et al. Self-powered ultra-flexible electronics via nano-grating-patterned organic photovoltaics. Nature 561, 516–521 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Williams, R. S., Goswami, S. & Goswami, S. Potential and challenges of computing with molecular materials. Nat. Mater. https://doi.org/10.1038/s41563-024-01820-4 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Shi, L., Zheng, G., Tian, B., Dkhil, B. & Duan, C. Research progress on solutions to the sneak path issue in memristor crossbar arrays. Nanoscale Adv. 2, 1811–1827 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, H. et al. Memristive crossbar arrays for storage and computing applications. Adv. Intell. Syst. 3, 2100017 (2021).

    Article 

    Google Scholar
     

  • Zoppo, G. et al. A mathematical analysis of wire resistance problem in memristor crossbars. In Proc. 2022 19th International SoC Design Conference (ISOCC) 249–250 (IEEE, 2022).

  • Lepri, N. et al. Modeling and compensation of IR drop in crosspoint accelerators of neural networks. IEEE Trans. Electron Devices 69, 1575–1581 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Hu, M., Strachan, J. P., Zhiyong, L., Stanley, R. & Williams, R. S. Dot-product engine as computing memory to accelerate machine learning algorithms. In International Symposium on Quality Electronic Design (ISQED) 374–379 (ISQED, 2016).

  • Hu, M. et. al. Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix-vector multiplication. In Proc. 53rd Annual Design Automation Conference 19:1–19:6 (DAC, 2016).

  • Shi, J., Yin, W., Osher, S. & Sajda, P. A fast hybrid algorithm for large-scale l1-regularized logistic regression. J. Mach. Learn. Res. 11, 713–741 (2010).

    MathSciNet 

    Google Scholar
     

  • Rupp, M., Tkatchenko, A., Müller, K.-R. & Von Lilienfeld, O. A. Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 108, 058301 (2012).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • McConaghy, T. in Genetic Programming Theory and Practice IX. Genetic and Evolutionary Computation (eds Riolo, R. et al.) 235–260 (Springer, 2011).

  • Acharya, J., Diakonikolas, I., Li, J. & Schmidt, L. Fast algorithms for segmented regression. In Proc. International Conference on Machine Learning 2878–2886 (PMLR, 2016).

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