# Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence
## 3 Overview of Emerging Memory Technologies
### Emerging Memory Technologies
#### 3.1.1 Filamentary Memory
They offered a microcontroller for portable healthcare, security equipment, or sensor processing applications. Renesas also investigated RRAM for low-power microcontroller units (MCUs) for IoT applications [52]. Intel [47; 55] and TSMC [56] both demonstrated RRAM based macros in 22 nm in a 1T1R configuration for embedded applications, with comparable features (85 \({}^{\circ}\)C 10-year retention, 10 kcycles endurance). Intel studies mobile and RF applications and TSMC targets eFlash, IoT, and smartcards. Adesto offers RRAM based EEPROM, as a compatible serial memory for discrete and embedded memory applications. They target IoT and other energy-conscious applications. The startup Crossbar is also very active and provides RRAM for IoT system-on-chips but also persistent memory solutions. In the case of embedded applications, the memory is integrated in the BEOL above the logic. In particular, the memory cell is integrated above the select transistor in the 1T1R configuration. The bitcell area is limited by the transistor more than by the memory itself [57]. Thus, it is important to have low operating voltages. Ideally, the RRAM would use logic transistors where the voltage was ~1 V. Due to the short RRAM programming time (~100 ns), the transistor could operate in overdrive mode [57]. This leads a targeted RRAM programming voltage of ~1.5 V to be compatible with logic CMOS to reach the best bitcell density. For more advanced nodes (1\(\times\) nm nodes), the memory could be integrated in a 1S1R configuration with a backend selector [58; 59] to reduce the bitcell size and target higher capacities than the 1T1R architecture allows [60]. The main issue for RRAM is the variability and related resistance distribution spread for high and low resistive states. Indeed, the conductive filament is composed with a finite number of atoms, and the memory operation is governed by stochastic phenomena [61; 62]. Thus, from cycle to cycle, the conductive filament can have various shapes and is constituted by an uncontrolled number of atoms leading to resistance variability. This resistance dispersion reduces the read window margin and limits the maximum memory capacity that can be achieved. In order to improve RRAM variability, various solutions have been investigated. The first is to improve the memory stack [63; 64]. In particular, \"subquantum\" CBRAM where filaments comprise a semiconductor or semimetal instead of a metal were proposed [65] in order to achieve thicker filaments, less affected by single events. The second approach is to adjust programming schemes and algorithms in order to reach sharper resistance distributions [66; 67]. Once RRAM variability is improved, larger capacities can be envisaged and new fields can be targeted. In particular, storage class memories (SCM) can be envisaged where the RRAM would be placed between the DRAM and storage memory in the hierarchy due to its high speed and good endurance. A few years ago, Micron presented a 16 Gb RRAM in a 27 nm node targeting SCM applications with excellent reliability, achieving 10\({}^{5}\) cycles with \(\mathcal{<}\)\(\mathcal{7}\times 10^{-5}\) of bit error rate due to optimized programming schemes [68]. No further work has reported from Micron but SONY also provides cross-point RRAM for storage class memory applications with an excellent widow margin of two decades at 38 [69]. Western Digital introduced RRAM in its roadmap [70] and announced \"RRAM SCM will close the gap in terms of per-GB cost with BiCS NAND and will thus widen the gap with DRAM, which will make it more economically feasible\". RRAM also has the potential to enable analog neuromorphic computing features. This could allow low power neuromorphic IP in embedded nonvolatile memory system-on-chip without adding additional process complexity [20]. More details are provided in the next section. In summary, RRAM is a proven technology with very low cost, ease of integration in the backend, suitable for embedded (smart card and IoT) and neuromorphic applications.