IEEE access, 2024-01, Vol.12, p.1-1
2024
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Details
- Autor(en) / Beteiligte
- Titel
- A Compact Model for Interface-Type Self-Rectifying Resistive Memory With Experiment Verification
- Ist Teil von
- IEEE access, 2024-01, Vol.12, p.1-1
- Ort / Verlag
- Piscataway: IEEE
- Erscheinungsjahr
- 2024
- Quelle
- EZB Free E-Journals
- Beschreibungen/Notizen
- A new non-volatile memory, resistive random access memory (RRAM), enables computein- memory (CIM)-based hardware accelerators with improved throughput and energy efficiency, further enabling machine learning instant-on inference at the edge. However, sneak-path currents in RRAM crossbar array (CBA) can cause crosstalk, limiting their high-density applications. Self-rectifying RRAMs (SRR) are the best choice for suppressing leakage currents. The interface-type RRAM provides CMOS compatibility, better controllability, high reliability, and lower power consumption compared to the filament-type. In this paper, for circuit and system exploration, a compact model of interface-type RRAM is developed. The model includes the Schottky barrier diode, effective layer resistance, nano-battery effect, and parasitic resistance and capacitance. Moreover, it has a dynamic behavior model such as device-to-device variation, retention, and endurance. It reproduces a high accuracy of 98.97% on DC and 98.05% on AC compared to the measurements. The proposed model is applied to a neuromorphic 64 × 64 SRR CBA with 32-bit fixedpoint precision. Moreover, nano-battery bias scheme is proposed and reduces the sneak-path current error to 0.02 %. Vector matrix multiplication (VMM) application shows 3.44 TOPS/W with an LRS to HRS ratio of 50:50, and deep neural network (DNN) of VGG-8 architecture with CIFAR-10 datasets observes that 1.36 % accuracy degradation.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2169-3536eISSN: 2169-3536DOI: 10.1109/ACCESS.2024.3349463Titel-ID: cdi_ieee_primary_10379796
- Format
- –
- Schlagworte
- Accuracy, Artificial neural networks, Behavioral sciences, Circuit and system simulation, Circuits, Circuits and systems, Compact model, crossbar array, Crosstalk, deep neural network (DNN), device-to-device variation, effective layer resistance, Electrodes, endurance, Fatigue tests, In-memory computing, Integrated circuit modeling, interface-type RRAM, Leakage current, Machine learning, Mathematical analysis, Matrix algebra, Microprocessors, multiply and accumulate (MAC), nano-battery effect, Parasitic capacitance, parasitic resistance, Power consumption, Random access memory, Resistance, resistive random access memory (RRAM), retention, Schottky barrier diode, Schottky diodes, self-rectifying RRAM (SRR), Switches, vector matrix multiplication (VMM)