# Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence
## 1 Introduction  
In the digital era, we are living through societal changes, carried by major technological revolutions. The amount of generated data is exponentially growing; in 2025, about 175 million terabytes will be generated, which would represent 10 times the volume produced in 2015 [1]. This trend comes with a large increase in connected objects and smart components and with the development of required data centers to cover the needs of social networks, streaming, and video on demand. Indeed, it is forecasted that in 2025 data centers will consume about 5% of the globe's produced energy [2], which will cause further issues. Moreover, we are living a change of paradigm where computing system energy consumption is limited more by data transfer than computation itself, leading to the rise of memory-centric and in-memory computing systems. In this context, pressure is exerted on the memory component to fulfill the new specifications of these emerging computing system architectures.  
Figure 1 presents a brief summary of the main milestones of nonvolatile memory history.  
The history of semiconductor memory began in 1984 when [PERSON] invented the NAND flash memory [3], leading to success in 1989, when Toshiba's first NAND flash reached the market [4]. NAND technology was scaled for decades, retaining the same concept, stack, and architecture, with the memory density increasing exponentially over time [5]. In 2001, various Flash players announced and launched MLC (multilevel cell) NAND, enabling a capacity increase [6].  
In 2007, Toshiba presented the first NAND integrated into 3D architecture [7], while Samsung announced in 2012 the first generation of 3D NAND.  
After 2010, embedded memories began to reach the 28-nm node [8], where traditional memory cell concepts became limited in terms of area shrinkage capability and increasing complexity. Then, pressure was exerted on emerging memory concepts in order to pursue scaling to more aggressive technology nodes, opening the path to new class of embedded technologies.  
In 2015, Intel and Micron developed the 3D XPoint Technology based on a phase change memory (PCM). This new class of memory enabled filling the latency gap between DRAM and Flash, often referred to as storage class memory. This made possible new system memory architectures with improved performances, bringing innovation to the nonvolatile memory (NVM) arena. The first announcement was made in 2015, and the technology has been available on the open market under the brand name Optane (Intel) since 2017 [9].  
We are now in the more than Moore era, and we work toward new systems (including in-memory computing and non von Neumann architectures) that emulate the human brain to achieve high energy efficiency, parallelism, and ability in cognitive tasks, such as object recognition, association, adaptation, and learning [10]. This offers a strong opportunity for new memory technologies.  
This article presents the evolution of memory technologies since the invention of Flash memory, and describes how the exponential increase in new systems (essentially coming from in-memory computing architectures, artificial intelligence, and neuromorphic circuits) offers opportunities to back-end emerging memory technologies. Section 2 presents the memory technologies' market and trends for both stand-alone and embedded sectors. Section 3 presents an overview of emerging memories with key demonstrators presented in the literature. Finally, Section 4 analyzes how emerging memories can be used in new computing systems, for both von Neumann and non von Neumann architectures.