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1. C. H. Lien et al., "Overview and high density application of HfOx based RRAM," 2014 12th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT), 2014, pp. 1-4, doi: 10.1109/ICSICT.2014..

2. Shafiee et al., "ISAAC: A Convolutional Neural Network Accelerator with In-Situ Arithmetic in Crossbars," 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), 2016, pp. 14-26, doi: 10.1109/ISCA.2016.12.

3. P. Chi et al., "PRIME: A Novel Processing-in-Memory Architecture for Neural Network Computation in ReRAM-Based Main Memory," 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), 2016, pp. 27-39, doi: 10.1109/ISCA.2016.13.

4. E. Brum et al., "Evaluating the Impact of Process Variation on RRAMs," 2021 IEEE 22nd Latin American Test Symposium (LATS), 2021, pp. 1-6, doi: 10.1109/LATS53581.2021..

5. F. M. Puglisi, C. Wenger and P. Pavan, "A Novel Program-Verify Algorithm for Multi-Bit Operation in HfO2 RRAM," in IEEE Electron Device Letters, vol. 36, no. 10, pp. 1030-1032, Oct. 2015, doi: 10.1109/LED.2015..

6. J. Chen, H. Wu, B. Gao, J. Tang, X. S. Hu and H. Qian, "A Parallel Multibit Programing Scheme With High Precision for RRAM-Based Neuromorphic Systems," in IEEE Transactions on Electron Devices, vol. 67, no. 5, pp. 2213-2217, May 2020, doi: 10.1109/TED.2020..

7. Z. Swaidan, R. Kanj, J. El Hajj, E. Saad and F. Kurdahi, "RRAM Endurance and Retention: Challenges, Opportunities and Implications on Reliable Design," 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2019, pp. 402-405, doi: 10.1109/ICECS46596.2019..

8. Z. Dong et al., "Convolutional Neural Networks Based on RRAM Devices for Image Recognition and Online Learning Tasks," in IEEE Transactions on Electron Devices, vol. 66, no. 1, pp. 793-801, Jan. 2019, doi: 10.1109/TED.2018..


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