Neuromorphic platforms are gaining recognition as a consequence of their superior effectivity, low energy consumption, and adaptable parallel sign processing capabilities, overcoming the restrictions of conventional von Neumann structure. we undertake a complete exploration of the determinants governing the resistive switching mechanism in memristor units based mostly on lead iodide (PbI2). We set up intricate connections between machine efficiency and morphological traits, subsequently revealing the machine’s synaptic-like habits, and making it amenable to a spread of versatile purposes Notably, a extremely dependable unipolar switching mechanism is recognized, exhibiting stability even below mechanical pressure (with a bending radius of roughly 4 mm) and in excessive humidity environments (at 75% relative humidity) with out the necessity for encapsulation. The investigation delves into the advanced interaction of cost transport and the energetic interface, elucidating the components contributing to the outstanding resistive switching noticed in PbI2-based memristors. The detailed findings spotlight synaptic behaviors akin to the modulation of synaptic strengths, with a formidable 2 x 104 cycles, emphasizing the function of spike time-dependent plasticity (STDP). The versatile platform demonstrates distinctive efficiency, attaining a simulated accuracy price of 95.06% in recognizing modified patterns from the Nationwide Institute of Requirements and Know-how (MNIST) dataset with simply 30 coaching epochs. Finally, this analysis underscores the potential of PbI2-based versatile memristor units as versatile elements for neuromorphic computing. Moreover, it emphasizes the resilience of PbI2 memristors when it comes to their resistive switching capabilities, each mechanically and electrically, highlighting their promise in emulating synaptic features for superior data processing techniques.
