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Synthesis and Characterization of Multi-Modal Phase-Change Porphyrin Droplets
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Speeding Up the Write Operation for Multi-Level Cell Phase Change Memory with Programmable Ramp-Down Current Pulses.

Chenchen Xie1,2, Xi Li3, Houpeng Chen2

  • 1Schools of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China.

Micromachines
|July 11, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new, efficient write operation for multi-level cell (MLC) phase change memory (PCM) using programmable current pulses. This method improves performance for artificial neural networks and reduces power consumption.

Keywords:
multi-level cellphase change memoryprogrammable ramp-down current pulses

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Multi-level cell (MLC) phase change memory (PCM) offers increased capacity and potential for artificial neural networks.
  • Current write and verify schemes for MLC PCM are time-consuming and power-intensive due to device variability.

Purpose of the Study:

  • To propose a novel, efficient write operation for MLC PCM.
  • To develop a high-performance read circuit for MLC PCM.
  • To demonstrate a 2-bit/cell PCM chip with improved write efficiency and fast read access.

Main Methods:

  • Utilizing programmable ramp-down current pulses for programming RESET initialized memory cells.
  • Employing a fully differential read circuit with an optional reference current source.
  • Implementing experiments to analyze resistance distribution and drift.

Main Results:

  • A novel write operation using single current pulses for efficient MLC PCM programming.
  • A 2-bit/cell PCM chip achieved a read access time of 65 ns.
  • Experimental validation of resistance distribution and drift characteristics.

Conclusions:

  • The proposed write operation significantly enhances MLC PCM efficiency.
  • The developed PCM chip demonstrates competitive performance for memory applications.
  • This advancement holds promise for next-generation artificial neural networks and memory devices.