A novel True Random Number Generator circuit fabricated in a 130 nm HfO ₂ -based resistive RAM process is presented. The generation of the random bit stream is based on a specific programming sequence applied to a dedicated memory array. In the proposed programming scheme, all the cells of the memory array are addressed at the same time while the current provided to the circuit is limited to program only a subset of the memory array, resulting in a stochastic distribution of cell resistance values. Some cells are switched in a low resistive state, other cells are slightly programmed to reach an intermediate resistance state, while the remaining cells maintain their initial high resistance state. Resistance values are next converted into a bit stream and confronted to National Institute of Standards and Technology (NIST) test benchmarks. The generated random bit stream has successfully passed twelve NIST tests out of fifteen. Compared to state-of-the-art resistive RAM-based true random number generators, our proposed methodology is the first one to leverage on programming current limitation at a memory array level.

Emerging non-volatile memories based on resistive switching mechanisms attract intense R&D efforts from both academia and industry. Oxide-based Resistive Random Acces Memories (OxRAM) gather noteworthy performances, such as fast write/read speed, low power and high endurance outperforming therefore conventional Flash memories. To fully explore new design concepts such as distributed memory in logic, OxRAM compact models have to be developed and implemented into electrical simulators to assess performances at a circuit level. In this paper, we present compact models of the bipolar OxRAM memory based on physical phenomenons. This model was implemented in electrical simulators for single device up to circuit level.

RRAM density enhancement is essential not only to gain market share in the highly competitive emerging memory sector but also to enable future high-capacity and power-efficient brain-inspired systems, beyond the capabilities of today’s hardware. In this paper, a novel design scheme is proposed to realize reliable and uniform multi-level cell (MLC) RRAM operation without the need of any read verification. RRAM quad-level cell (QLC) capability with 4 bits/cell is demonstrated for the first time. QLC is implemented based on a strict control of the cell programming current of 1T-1R HfO₂-based RRAM cells. From a design standpoint, a self-adaptive write termination circuit is proposed to control the RESET operation and provide an accurate tuning of the analog resistance value of each cell of a memory array. The different resistance levels are obtained by varying the compliance current in the RESET direction. Impact of variability on resistance margins is simulated and analyzed quantitatively at the circuit level to guarantee the robustness of the proposed MLC scheme. The minimal resistance margin reported between two consecutive states is 2.1 kΩ along with an average energy consumption and latency of 25 pJ/cell and 1.65 μs, respectively.

Energy efficiency remains one of the main factors for improving the key performance markers of RRAMs to support IoT edge devices. This paper proposes a simple and feasible low power design scheme which can be used as a powerful tool for energy reduction in RRAM circuits. The design scheme is exclusively based on current control during write and read operations and ensures that write operations are completed without wasted energy. Self-adaptive write termination circuits are proposed to control the RRAM current during FORMING, RESET and SET operations. The termination circuits sense the programming current and stop the write pulse as soon as a preferred programming current is reached. Simulation results demonstrate that an appropriate choice of the programming currents can help obtain 4.1X improvement in FORMING, 9.1X improvement in SET and 1.12X improvement in RESET energy. Also, the possibility to have a tight control over the RESET resistance is demonstrated. READ energy optimization is also covered by leveraging on a differential sense amplifier offering a programmable current reference. Finally, an optimal trade-off between energy consumption during SET/RESET operations and an acceptable read margin is established according to the final application requirements.

Aerospace applications are attractive candidates to embed artificial neural networks despite their excellent parallel processing capability and reduced energy consumption. Nonetheless, the long-term exposure to incidence levels of ionizing radiation may degrade their physical components reducing, therefore, their reliability and expected lifetime. Thus, it is mandatory to face the challenge of enhancing the radiation hardening characteristics of a neural circuit before operating in harsh environments. A possible solution to substantially reduce long-term spurious effects caused by ionizing radiation [referred to as total ionizing dose (TID)] is to change the conventional rectangular MOS gate geometry to a nonstandard topology referred to as an enclosed layout transistor (ELT). In the context of hardening a complete neuron circuit against TID effects, together with the well-established ELT paradigm, it is possible to exploit the inclusion of other hardened devices, for instance, the memory element. In this sense, the Oxide-based Resistive Random Access Memory (OxRAM) can be used as the memory element, which is inherently tolerant against ionizing radiation and, hence, better suited for a fully hardened circuit. In this work, we propose to harden the design of an existing OxRAM-based neuron circuit through the inclusion of ELTs, i.e., to improve the radiation hardening characteristics of a preexistent convenient neuron circuit topology by using the enclosed gate geometry for the nMOS and pMOS devices. Electrical simulations, considering a standard commercial bulk CMOS fabrication process, in a 180-nm technology, have been carried out to validate our proposed design. In addition, we exploit two simulation setups: first, the OxRAM's behavior in a simple circuit configuration, to provide a better understanding of the OxRAM device; second, the OxRAM-based neuron circuit, to evaluate the behavior of the proposed neuron circuit hardened with ELTs. The simulation results, supported by the analysis of former works regarding the incidence of ionizing radiation in OxRAM and ELTs, indicate that the proposed hardened neuron circuit is a feasible solution to embed neuromorphic computing in aerospace applications.