Resistive switching memories (ReRAM) have been widely studied for applications in next-generation data storage and neurormorphic computing systems. To enable device-circuit-system co-design and optimization, a SPICE model of ReRAM that can reproduce the device characteristics in circuit simulations is needed. In this paper, we present a novel tool for ReRAM design including a physics-based SPICE model, the model parameters extraction strategy, as well as the system assessment method. This physics-based SPICE model can capture all the essential features of HfOx-based ReRAM including the DC/AC and multi-level switching behaviors, switching reliability, and intrinsic device variations. A strategy is developed to extract the critical model parameters from the fabricated ReRAM devices. A variety of electrical measurements on various ReRAMs are performed to verify and calibrate the model. The assessment method based on the experimentally verified SPICE model can be applied to explore a wide range of applications including: 1) variation-aware and reliability-emphasized system design; 2) system performance evaluation; 3) array architecture optimization. This verified design tool not only enables system design but also enables system optimization that capitalizes on device/circuit interaction for both data storage and neuromorphic computing applications.
[1]
Shimeng Yu,et al.
Metal–Oxide RRAM
,
2012,
Proceedings of the IEEE.
[2]
B. Gao,et al.
A Physics-Based Compact Model of Metal-Oxide-Based RRAM DC and AC Operations
,
2013,
IEEE Transactions on Electron Devices.
[3]
Bing Chen,et al.
A SPICE Model of Resistive Random Access Memory for Large-Scale Memory Array Simulation
,
2014,
IEEE Electron Device Letters.
[4]
Kailash Gopalakrishnan,et al.
Overview of candidate device technologies for storage-class memory
,
2008,
IBM J. Res. Dev..
[5]
D. Ielmini,et al.
Self-Accelerated Thermal Dissolution Model for Reset Programming in Unipolar Resistive-Switching Memory (RRAM) Devices
,
2009,
IEEE Transactions on Electron Devices.