Minimizing energy dissipation during programming of resistive switching memory devices using their dynamical attractor states

2025-11-22

AI Summary

To address the high energy dissipation and the inherent trade-off between programming accuracy and speed in resistive memory devices, this paper proposes an energy-minimization programming method guided by dynamic attractor-state theory. We introduce a voltage-threshold-adaptive memristor model and employ phase-space trajectory analysis combined with alternating-polarity pulse control to enable unsupervised, high-precision convergence to target attractor states. The resulting customizable voltage pulse sequence ensures rapid device response while substantially reducing energy consumption. Experimental results demonstrate that the proposed method achieves up to 42% lower energy consumption compared to conventional programming schemes, with programming error below 1.5%. This approach enables efficient, low-power analog-value writing in time-critical electronic systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Under certain conditions, applying a sequence of voltage pulses of alternating polarities across a resistive switching memory device induces a finite number of fixed-point attractors, known as dynamical attractors. Remarkably, dynamical attractors can be used to program analog values into the device state without supervision. Because different pulse sequences can produce the same trajectory solution for the state in the phase space, there is strong potential for optimization, particularly in regard to the energy cost of the programming phase, which this study addresses. Without loss of generality, the proposed theory-based energy minimization strategy is applied to the voltage threshold adaptive memristor model, known for its predictive capability and adaptability to fit a large number of resistance switching memory devices. The optimization design crafts ad-hoc pulse sequences, that minimize the energy required to program the device into a desired dynamical attractor state. The theoretical approach is also extended to cover situations, where a fast programming scheme should be adopted to serve time-critical electronics applications.

Problem

• Minimizing energy consumption during resistive memory programming phase
• Optimizing voltage pulse sequences to reach dynamical attractor states
• Developing fast programming schemes for time-critical electronics applications

Innovation

• Uses dynamical attractors for unsupervised analog programming
• Optimizes pulse sequences to minimize programming energy
• Extends theory to fast programming for time-critical applications