Uncovering Phase Change Memory Energy Limits by Sub‐Nanosecond Probing of Power Dissipation Dynamics. (12th June 2021)

• Record Type: Journal Article
• Authors: Stern, Keren; Wainstein, Nicolás; Keller, Yair; Neumann, Christopher M.; Pop, Eric; Kvatinsky, Shahar; Yalon, Eilam
• Is Part Of: Advanced Electronic Materials. Volume 7:Number 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-12
• Subjects: energy efficiency -- non‐volatile memory -- phase change materials -- sub‐nanosecond probing -- thermal management; Materials -- Electric properties -- Periodicals; Materials science -- Periodicals; Magnetic materials -- Periodicals; Electronic apparatus and appliances -- Periodicals; 537
• DOI: 10.1002/aelm.202100217
• Languages: English
• ISSNs: 2199-160X

Abstract

Abstract: Phase change memory (PCM) is one of the leading candidates for neuromorphic hardware and has recently matured as a storage class memory. Yet, energy and power consumption remain key challenges for this technology because part of the PCM device must be self‐heated to its melting temperature during reset. Here, it is shown that this reset energy can be reduced by nearly two orders of magnitude by minimizing the pulse width. A high‐speed measurement setup is utilized to probe the energy consumption in PCM cells with varying pulse width (0.3–40 nanoseconds) and uncover the power dissipation dynamics. A key finding is that the switching power ( P ) remains unchanged for pulses wider than a short thermal time constant of the PCM (τth < 1 ns in 50 nm diameter device), resulting in a decrease of energy ( E = P × τ) as the pulse width τ is reduced in that range. Thermal confinement during short pulses is achieved by limiting the heat diffusion time. The improved programming scheme reduces reset energy density below 0.1 nJ µm −2, over an order of magnitude lower than state‐of‐the‐art PCM, potentially changing the roadmap of future data storage technology and paving the way toward energy‐efficient neuromorphic hardware. Abstract : Phase change memory is a leading candidate for neuromorphic hardware, yet energy consumption remains a key challenge. A specialized high‐speed measurement setup is utilized here to probe the power and energy consumption with sub‐ns temporal resolution. It is found that sub‐ns programming improves the energy efficiency by over an order of magnitude compared with the state of the art.