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Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour

Nature Materials volume 21, pages 1403–1411 (2022)Cite this article

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Abstract

To realize molecular-scale electrical operations beyond the von Neumann bottleneck, new types of multifunctional switches are needed that mimic self-learning or neuromorphic computing by dynamically toggling between multiple operations that depend on their past. Here, we report a molecule that switches from high to low conductance states with massive negative memristive behaviour that depends on the drive speed and number of past switching events, with all the measurements fully modelled using atomistic and analytical models. This dynamic molecular switch emulates synaptic behavior and Pavlovian learning, all within a 2.4-nm-thick layer that is three orders of magnitude thinner than a neuronal synapse. The dynamic molecular switch provides all the fundamental logic gates necessary for deep learning because of its time-domain and voltage-dependent plasticity. The synapse-mimicking multifunctional dynamic molecular switch represents an adaptable molecular-scale hardware operable in solid-state devices, and opens a pathway to simplify dynamic complex electrical operations encoded within a single ultracompact component.

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Fig. 1: The dynamic molecular junction.
Fig. 2: Mechanism of PCET.
Fig. 3: Electrical characteristics of the Au-S-C10-HATNA//Ga2O3/EGaIn junctions.
Fig. 4: Model of the dynamic switch.
Fig. 5: Synaptic function.
Fig. 6: Demonstration of Pavlovian learning and Boolean logic gates using time plasticity in the DMS.

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Data availability

Data are available in the supplementary materials and on https://dataverse.harvard.edu/privateurl.xhtml?token=efd1a016-cc24-41ab-891b-5217fa6dd56d. Source data are provided with this paper.

Code availability

Codes are available from Harvard Dataverse at https://dataverse.harvard.edu/privateurl.xhtml?token=efd1a016-cc24-41ab-891b-5217fa6dd56d.

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Acknowledgements

We thank the Ministry of Education (MOE, awards no. MOE2018-T2-1-088 and no. MOE2019-T2-1-137) and the Prime Minister’s Office, Singapore, under its Medium Sized Centre program for supporting this research. D.T. acknowledges support from Science Foundation Ireland (SFI) under awards no. 15/CDA/3491 and no. 12/RC/2275_P2 and supercomputing resources at the SFI/Higher Education Authority Irish Center for High-End Computing (ICHEC). E.d.B. and C.N. acknowledge support from the US National Science Foundation (grant no. ECCS#1916874). D.Q. acknowledges the support of the Australian Research Council (grant no. FT160100207). C.H. and B.B. gratefully acknowledge funding from the Deutsche Forschungsgemeinschaft (German Research Foundation) Project-ID 433682494-SFB 1459.

Author information

Author notes

  1. Qian Zhang

    Present address: School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, China

  2. These authors contributed equally: Yulong Wang, Qian Zhang, Hippolyte P. A. G. Astier, Cameron Nickle.

Authors and Affiliations

  1. Department of Chemistry, National University of Singapore, Singapore, Singapore

    Yulong Wang, Qian Zhang, Hippolyte P. A. G. Astier, Ziyu Zhang, Andrea Leoncini, Yingmei Han & Christian A. Nijhuis

  2. Department of Physics, University of Central Florida, Orlando, FL, USA

    Cameron Nickle & Enrique del Barco

  3. Hybrid Materials for Opto-Electronics Group, Department of Molecules and Materials, MESA+Institute for Nanotechnology, Molecules Center and Center for Brain-Inspired Nano Systems, Faculty of Science and Technology, University of Twente, Enschede, the Netherlands

    Saurabh Soni, Fuad A. Alami, Alessandro Borrini & Christian A. Nijhuis

  4. Institute of Physical Chemistry, Westfälische Wilhelms-Universität Münster, Münster, Germany

    Christian Honnigfort & Björn Braunschweig

  5. Center of Soft Nanoscience, Westfälische Wilhelms-Universität Münster, Münster, Germany

    Christian Honnigfort & Björn Braunschweig

  6. School of Chemistry and Physics, Queensland University of Technology, Brisbane, Queensland, Australia

    Dong-Cheng Qi

  7. Department of Physics, Bernal Institute, University of Limerick, Limerick, Ireland

    Damien Thompson

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Contributions

C.A.N. conceived and supervised the project. D.T. conducted the atomistic modelling. E.d.B. and C.N. conducted the numerical modelling. Y.W. and A.B. synthesized the compounds, Y.W. and Y.H. performed the CV and ultraviolet–visible measurements. Z.Z. and D.-C.Q. performed the angle-resolved XPS and near-edge X-ray absorption fine structure measurements and analysis. Y.W. and Q.Z. performed the I–V electrical measurements. H.P.A.G.A. designed, performed and analysed the synaptic emulation measurements. S.S. and F.A.A. conducted the millisecond-scale pulse experiments. C.H. and B.B. conducted and analysed the sum-generation frequency experiments. A.L. developed the Origin code for electrical data analysis. C.A.N., E.d.B., D.T. and H.P.A.G.A. wrote the manuscript and all the authors commented on it.

Corresponding authors

Correspondence to Enrique del Barco, Damien Thompson or Christian A. Nijhuis.

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Extended data

Extended Data Fig. 1 The periodic DFT calculations.

a, The calculated PDOS of Au-bound S-C10-HATNA with the nitrogen sites plotted as a thick black line. The full molecule is shown as a dashed grey line and contributions from carbon, hydrogen, oxygen, and sulphur sites are plotted as green, red, purple, and blue lines, respectively. b, The calculated PDOS of post-PCET Au-S-C10-(H6-HATNA). (c) Computed surfaces of the empty electron-acceptor LUMO → LUMO + 2 levels in Au-S-C10-HATNA together with the mid-gap state created at +1.1 eV in the first protonation step generating Au-S-C10-H+-HATNA. d, The calculated PDOS on the molecule of HATNA SAMs on Au(111) for all oxidation and protonated states.

Source data

Extended Data Fig. 2 Measurement of potentiation as a function of negative pulse amplitude as shown in Fig. 5b.

a-b, I(V) profile of the junction used in the measurement in linear (a) and logarithmic scale (b). c-e, pulse sequence used in the measurement with current (c), logarithm of current (d) and voltage (e) as a function of time, with colour used to help distinguish sections of the sequence. A first negative voltage pulse of amplitude Vp (with tp = 1 s) was applied, a delay at 0 V was set for td = 1 s, and then a second negative voltage pulse of magnitude Vp was applied, followed by a series of positive voltage pulses of amplitude Vr = 1.25 V to reset the junction. This sequence was repeated each time varying the amplitude of the negative voltage pulses Vp from 0.5 V to 2 V and back. After half a sequence to train the junction, the current amplitude of the second negative pulse was compared to that of the first negative pulse (using the first data point of each pulse) to determine the potentiation as a function of Vp. Time t = 0 corresponds to the time the measurement was started, immediately after junction formation.

Source data

Extended Data Fig. 3 Measurement of potentiation as a function of negative pulse duration as shown in Fig. 5c.

a-b, I(V) profile of the junction used in the measurement in linear (a) and logarithmic scale (b). c-e, pulse sequence used in the measurement with current (c), logarithm of current (d) and voltage (e) as a function of time, with colour used to help distinguish sections of the sequence. A first negative voltage pulse of amplitude Vp = -2V and duration tp was applied, a delay at 0 V was set for td = 1 s, a second negative voltage pulse of magnitude Vp was applied, followed by a series of positive voltage pulses of amplitude Vr = 1.25 V to reset the junction. This sequence was repeated each time varying Vp the amplitude of the negative voltage pulses from 0.5 V to 2 V and back, and after half a sequence to train the junction, the current amplitude of the second negative pulse was compared to that of the first negative pulse to determine the potentiation as a function of Vp. Time t = 0 corresponds to the time the measurement was started, immediately after junction formation.

Source data

Extended Data Fig. 4 Measurement of potentiation as a function of inter-pulse delay td as shown in Fig. 5d.

a-b, I(V) profile of the junction used in the measurement in linear (a) and logarithmic scale (b). c-e, pulse sequence used in the measurement with current (c), logarithm of current (d) and voltage (e) as a function of time, with colour used to help distinguish sections of the sequence. A first negative pulse was applied, followed by a delay of duration td at 1.25 V, then a second pulse, and finally a positive pulse is applied to reset the junction. Each pulse pair was used to extract two data points: one with td > 0 and one with td < 0, whereby a positive (negative) value of td signifies that the reference pulse occurs before (after) the measured pulse. The relative potentiation was measured as the ratio of the difference between the current of the measured pulse and the current of the reference pulse (here using the values of current in the middle of the pulses) to the current of the reference pulse. Time t = 0 corresponds to the time the measurement was started, immediately after junction formation.

Source data

Extended Data Fig. 5 Measurement of potentiation as a function of positive pulse duration as shown in Fig. 5e.

a-b, I(V) profile of the junction used in the measurement in linear (a) and logarithmic scale (b). c-e, pulse sequence used in the measurement with current (c), logarithm of current (d) and voltage (e) as a function of time, where colour is used to help distinguish sections of the sequence. A negative reference voltage pulse (Vp = -1.5 V, tp = 1 s) was applied, a positive voltage pulse of varying amplitude (duration td = 2 s) was applied, and a second negative voltage pulse (Vp = -1.5 V, tp = 1 s) was applied. This sequence was repeated each time varying Vr the amplitude of the positive voltage pulse from 0.5 V to 2 V and back, and after one training sequence (~500 s), the current amplitude of the second negative pulse was compared to that of the reference negative pulse to determine the potentiation as a function of Vr. Time t = 0 corresponds to the time the measurement was started, immediately after junction formation.

Source data

Extended Data Fig. 6 Voltage cycling measurements for the junctions.

Voltage cycling showing 100 000 voltage pulses with amplitudes of -2 V and +1 V and pulse time of 20 µs (black) and current response (red); b, zoomed-in version of the voltage cycling data in a to show the pulse parameters and current response more clearly; c, Measurement of potentiation as a function of short positive voltage pulses of +1 V amplitude and 2 ms pulse time period (black), and corresponding current response of the junction (red) showing PPD. Due to the basic nature of HATNA, HATNA can be protonated representing the on-state (that is, the (HATNA)H+ state as explained in the main text and shown in Fig. 2f-g (and experimentally proven with vibrational sum-frequency generation spectroscopy of HATNA monolayers described in Supplementary Section S3.3). A series of short positive voltages deprotonates HATNA turning it off which is observed as reduction in the current response, as seen here. More experimental details on the pulse parameters are given in Supplementary Information Section S5.3.

Source data

Extended Data Fig. 7 Measurement demonstrating the Pavlov experiment (Hebbian learning).

a-b, I(V) profile of the junction used in the measurement in linear (a) and logarithmic scale (b). c, pulse sequence showing Hebbian learning (from Fig. 6b-e). The e-ANN works as follows: two source-measure units represent two artificial neurons N1 and N2 delivering voltage inputs in separate channels feeding into a third channel (see Fig. 6a) N3 where the current received is the output. N1 is connected via a fixed resistor (1kΩ) to N3 and gives the output unconditionally. N2 includes the junction in the off state such that pulses from N2 do not initially result in a substantial signal in N3. It corresponds to ‘dog hears bell’. However, firing pulses in N1 and N2 results in a PPF process in N2, ‘training’ it to deliver an output in N3 when pulses are fired from N2 thereafter. The sequences of voltage pulses are as follows. N1 alternates between pulses at -1 V and -1.1 V, each lasting 2 s, repeated 5 times. N2 alternates between pulses at +1.5 V and -1.1 V, each lasting 2 s, repeated 5 times. When pulses are applied from N1 and N2 simultaneously, the voltage across the dynamic switch can be estimated to be alternating between +2.4 V and -0.1 V approximately, according to Kirchhoff’s circuit laws, which excites the junction (enhances its conductance) and thus the coupling to N3.

Source data

Extended Data Fig. 8 Demonstration of remaining logic gates.

a, XOR; b, NAND; c, NOR; d, NXOR, as per the operation system described in the main text and Fig. 6, with current and voltage traces and truth tables. More details on the pulse parameters are given in Supplementary Information Section S5.2.3.

Source data

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Wang, Y., Zhang, Q., Astier, H.P.A.G. et al. Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour. Nat. Mater. 21, 1403–1411 (2022). https://doi.org/10.1038/s41563-022-01402-2

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