Phase Change Memory: A Review on Electrical Behavior and Use in Analog In‐Memory‐Computing (A‐IMC) Applications

Abstract

Abstract Recent development and progress of Artificial Intelligence (AI) algorithms made clear that this topic is a paradigm shift with respect to the past. High throughput and ability to do complex tasks makes AI a great field of opportunity. This advancement is somehow limited by the physical implementation of the chips that are still bound to the historical von‐Neumann Architecture with processing units and memory hardware spatially separated. The way data is bussed and processed needs disruptive innovation, rather than an evolutionary approach, too. In Analog In‐Memory Computing (A‐IMC) the typical properties of resistance‐based memory technologies are used to both store and compute information. This allows for incredibly high parallelism and removes the problems related to the known von‐Neumann bottleneck. In the present work, A‐IMC networks based on resistive memories and on the Phase Change Memory (PCM) technology, in particular, are extensively discussed. After a first review of the general features of PCM devices, their application to A‐IMC is described, aiming at a full description of the current technological scenario.

Keywords

• A‐IMC
• ePCM
• GST
• Ge‐GST
• MVM
• non‐idealities compensation