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Updated: Jul 20, 2025

Gradient Echo Quantum Memory in Warm Atomic Vapor

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

Electrical programmable multilevel nonvolatile photonic random-access memory.

Jiawei Meng¹, Yaliang Gui¹, Behrouz Movahhed Nouri¹

¹Department of Electrical and Computer Engineering, George Washington University, Washington DC, 20052, USA.

Light, Science & Applications

|August 1, 2023

Summary

This summary is machine-generated.

Researchers developed a low-loss, nonvolatile photonic random-access memory (P-RAM) using a novel phase-change material. This advancement enables efficient, multi-bit photonic computing with reduced power consumption and improved signal integrity.

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Area of Science:

Photonics
Materials Science
Computer Engineering

Background:

Photonic Random-Access Memories (P-RAM) are crucial for on-chip photonic computing, aiming to reduce optoelectronic conversion losses.
Existing phase-change material (PCM) based P-RAMs suffer from high optical loss and inefficient programming, hindering practical applications.

Purpose of the Study:

To demonstrate a multi-state, electrically programmed, low-loss nonvolatile photonic memory.
To utilize a broadband transparent phase-change material (Ge2Sb2Se5, GSSe) with minimal absorption in its amorphous state.

Main Methods:

Fabrication of a multi-bit P-RAM on a silicon-on-insulator platform using GSSe.
Electrical programming and characterization of memory states, including amplitude modulation and insertion loss.
Optimization of microheater placement and cyclability testing.

Main Results:

Demonstrated a zero-static-power, multi-bit P-RAM with efficient amplitude modulation (0.2 dB/μm).
Achieved ultralow insertion loss (0.12 dB for a 4-bit memory), representing a 100x improvement in signal-to-loss ratio over existing PCMs.
Validated device robustness with over half a million switching cycles.

Conclusions:

The developed GSSe-based P-RAM offers a low-loss, nonvolatile solution for photonic memory.
This technology supports photonic functional and programmable circuits for applications like neural networks, LiDAR, and sensors.