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    This study presents passive neuromorphic classifiers for enhanced circuit density. These passive networks achieve high accuracy in recognizing digits, offering a promising alternative to traditional methods.

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    Area of Science:

    • Nanotechnology
    • Neuromorphic Computing
    • Circuit Design

    Background:

    • Bottom-up fabrication offers superior circuit density and 3D integrability over traditional CMOS.
    • Stochasticity in bottom-up fabrication hinders complex circuit construction.
    • Passive components provide higher device densities and registration tolerances, suitable for nanocrossbars.

    Purpose of the Study:

    • To explore neuromorphic classifiers utilizing passive neurons and synapses.
    • To demonstrate the feasibility of passive components in complex computational tasks.
    • To address challenges in implementing negative weights and enhance fault tolerance.

    Main Methods:

    • Utilizing SPICE simulations to model a shallow network of passive rectifier neurons and resistive voltage summers.
    • Developing weight-to-conductance mappings for hardware implementation of negative weights.
    • Evaluating the impact of soft and hard defects on classification performance.

    Main Results:

    • A passive neuromorphic classifier recognized MNIST digits with 95.4% accuracy.
    • A method for implementing negative weights without significant memory overhead was introduced.
    • Fault tolerance was boosted through proposed methods, and performance was evaluated against benchmarks.

    Conclusions:

    • Passive neuromorphic classifiers show significant potential for high-density, low-power computing.
    • The proposed design overcomes limitations of passive components for complex tasks like digit recognition.
    • Further research can optimize these classifiers for real-world applications.