Research ArticleMATERIALS SCIENCE

Ionic decision-maker created as novel, solid-state devices

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Science Advances  07 Sep 2018:
Vol. 4, no. 9, eaau2057
DOI: 10.1126/sciadv.aau2057
  • Fig. 1 Theoretical background and experimental setup of ionic decision-maker.

    (A) 1: Original MBP, in which a gambler attempts to select a slot machine. 2: MBP in the channel model, in which a communication network user attempts to select an available channel. (B) Example of the results for users 1 and 2. (C) Illustration of the charge-conserving TOW principle used. (D) Illustration of the experimental setup using a two-terminal electrochemical cell, potentio/galvanostat, and a random number generator. The illustration is simplified. Details of the setup are described in Materials and Methods.

  • Fig. 2 Adaptive operation of ionic decision-maker for DMBPs.

    (A) Variation in cell voltage during experiment. (B) Illustration of variation in CSR versus number of selections. (C) Top: Variation in CSR of ionic decision-maker against Pi inversions occurring every 200 selections. CSR starts at close to 0.5, corresponding to completely random selection. Although CSR reached 1 within 100 selections with initial conditions (0.9, 0.1), (0.8, 0.2), and (0.7, 0.3), it did not exceed 0.95 with (0.6, 0.4) even within 200 selections because of relatively close probabilities. Bottom: Variation in number of packets.

  • Fig. 3 Theoretical background and experimental setup for competitive DMBPs.

    (A) Examples of two situations in which two network users attempt to use different channels or the same channel in the network (that is, competitive DMBP). (B) Illustration of ionic decision-maker composed of two electrochemical cells (devices 1 and 2), each with three electrodes, which are connected to potentio/galvanostat via a switch matrix.

  • Fig. 4 Adaptive operation of ionic decision-maker for competitive DMBPs.

    (A) Variation in selection rates for channels for two devices measured over 20 cycles for averaging. Initial probabilities of channels (PA, PB, and PC) were set to (0.9, 0.4, and 0.2), and the assignment was changed at every 100th selection. (B) Variation in total number of packets for devices 1 and 2 in two operation modes (combined devices and independent devices). SM and NE show theoretical limits of SM (pink dashed curve) and NE (gray dashed curve).

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/9/eaau2057/DC1

    Section S1. Fabrication of electrochemical cells for ionic decision-maker

    Section S2. Comparison with CPU-based computation using mathematical algorithms and dielectric capacitor

    Section S3. Operation mechanism of ionic decision-maker

    Section S4. Theoretical expectation of decision-making behavior with two devices

    Section S5. Two operation modes of ionic decision-maker for competitive MBPs with two devices and three channels

    Fig. S1. Fabrication of electrochemical cells for ionic decision-maker.

    Fig. S2. Comparison with CPU-based computation using mathematical algorithms and dielectric capacitor.

    Fig. S3. Operation mechanism and built-in “α” of ionic decision-maker.

    Fig. S4. Expected behavior of two devices and corresponding variation in selection rates for each channel for both devices.

    Fig. S5. Two operation modes of ionic decision-maker for competitive MBPs with two devices and three channels.

  • Supplementary Materials

    This PDF file includes:

    • Section S1. Fabrication of electrochemical cells for ionic decision-maker
    • Section S2. Comparison with CPU-based computation using mathematical algorithms and dielectric capacitor
    • Section S3. Operation mechanism of ionic decision-maker
    • Section S4. Theoretical expectation of decision-making behavior with two devices
    • Section S5. Two operation modes of ionic decision-maker for competitive MBPs with two devices and three channels
    • Fig. S1. Fabrication of electrochemical cells for ionic decision-maker.
    • Fig. S2. Comparison with CPU-based computation using mathematical algorithms and dielectric capacitor.
    • Fig. S3. Operation mechanism and built-in “α” of ionic decision-maker.
    • Fig. S4. Expected behavior of two devices and corresponding variation in selection rates for each channel for both devices.
    • Fig. S5. Two operation modes of ionic decision-maker for competitive MBPs with two devices and three channels.

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