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A flexible digital compute-in-memory chip for edge intelligence

  • Takeda, Y. et al. Fabrication of ultra-thin printed organic TFT CMOS logic circuits optimized for low-voltage wearable sensor applications. Sci. Rep. 6, 25714 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Musk, E. An integrated brain-machine interface platform with thousands of channels. J. Med. Internet Res. 21, e16194 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • McGlynn, E. et al. The future of neuroscience: flexible and wireless implantable neural electronics. Adv. Sci. 8, 2002693 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Zhan, Y., Mei, Y. & Zheng, L. Materials capability and device performance in flexible electronics for the Internet of Things. J. Mater. Chem. C 2, 1220–1232 (2014).

    Article 
    CAS 

    Google Scholar
     

  • Khan, A. I., Keshavarzi, A. & Datta, S. The future of ferroelectric field-effect transistor technology. Nat. Electron. 3, 588–597 (2020).

    Article 

    Google Scholar
     

  • Liu, Z. et al. A three-dimensionally architected electronic skin mimicking human mechanosensation. Science 384, 987–994 (2024).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Jung, Y. H. et al. A wireless haptic interface for programmable patterns of touch across large areas of the skin. Nat. Electron. 5, 374–385 (2022).

    Article 

    Google Scholar
     

  • Jiang, Y. et al. A universal interface for plug-and-play assembly of stretchable devices. Nature 614, 456–462 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • He, J. et al. Scalable production of high-performing woven lithium-ion fibre batteries. Nature 597, 57–63 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Shi, R. et al. An integrated analog front-end system on flexible substrate for the acquisition of bio-potential signals. Adv. Sci. 10, 2207683 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Papadopoulos, N. et al. Touchscreen tags based on thin-film electronics for the Internet of Everything. Nat. Electron. 2, 606–611 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, W. et al. Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin. Science 380, 735–742 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Dembo, H. et al. RFCPUs on glass and plastic substrates fabricated by TFT transfer technology. In IEEE International Electron Devices Meeting (IEDM) 125–127 (IEEE, 2005).

  • Karaki, N. et al. A flexible 8b asynchronous microprocessor based on low-temperature poly-silicon TFT technology. In IEEE International Solid-State Circuits Conference (ISSCC) 272–589 (IEEE, 2005).

  • Kurokawa, Y. et al. UHF RFCPUs on flexible and glass substrates for secure RFID systems. IEEE J. Solid State Circuit 43, 292–299 (2008).

    Article 
    ADS 

    Google Scholar
     

  • Geng, D. et al. Thin-film transistors for large-area electronics. Nat. Electron. 6, 963–972 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Yan, A. et al. Thin-film transistors for integrated circuits: fundamentals and recent progress. Adv. Funct. Mater. 34, 2304409 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Biggs, J. et al. A natively flexible 32-bit Arm microprocessor. Nature 595, 532–536 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Celiker, H., Dehaene, W. & Myny, K. Multi-project wafers for flexible thin-film electronics by independent foundries. Nature 629, 335–340 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ozer, E. et al. Bendable non-silicon RISC-V microprocessor. Nature 634, 341–346 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ozer, E. et al. A hardwired machine learning processing engine fabricated with submicron metal-oxide thin-film transistors on a flexible substrate. Nat. Electron. 3, 419–425 (2020).

    Article 

    Google Scholar
     

  • Ozer, E. et al. Malodour classification with low-cost flexible electronics. Nat. Commun. 14, 777 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Si, J. et al. A carbon-nanotube-based tensor processing unit. Nat. Electron. 7, 684–693 (2024).

    Article 

    Google Scholar
     

  • Liu, J. et al. TFT-based near-sensor in-memory computing: circuits and architecture perspectives of large-area eDRAM and ROM CiM chips. IEEE Trans. Circuits Syst. I 71, 620–633 (2024).


    Google Scholar
     

  • Tang, W. et al. Low-power and scalable BEOL-compatible IGZO TFT eDRAM-based charge-domain computing. IEEE Trans. Circuits Syst. I 70, 5166–5179 (2023).


    Google Scholar
     

  • Sun, Z. et al. A full spectrum of computing-in-memory technologies. Nat. Electron. 6, 823–835 (2023).

    Article 

    Google Scholar
     

  • Grossar, E., Stucchi, M., Maex, K. & Dehaene, W. Read stability and write-ability analysis of SRAM cells for nanometer technologies. IEEE J. Solid State Circuit 41, 2577–2588 (2006).

    Article 
    ADS 

    Google Scholar
     

  • Liu, A. et al. Selenium-alloyed tellurium oxide for amorphous p-channel transistors. Nature 629, 798–802 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Myny, K. The development of flexible integrated circuits based on thin-film transistors. Nat. Electron. 1, 30–39 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Shulaker, M. et al. Carbon nanotube computer. Nature 501, 526–530 (2013).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hills, G. et al. Modern microprocessor built from complementary carbon nanotube transistors. Nature 572, 595–602 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Shen, Y., Liu, X., Guo, X., Zhang, W. & Zhu, X. The back-channel effect in low temperature poly-Si thin film transistors. In SID Symposium Digest of Technical Papers 1418–1420 (IEEE, 2024).

  • Yan, B. et al. A 1.041-Mb/mm2 27.38-TOPS/W signed-INT8 dynamic-logic-based ADC-less SRAM compute-in-memory macro in 28nm with reconfigurable bitwise operation for AI and embedded applications. In IEEE International Solid-State Circuits Conference (ISSCC) 188–190 (IEEE, 2022).

  • Moody, G. & Mark, R. The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhong, Y. et al. A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing. Nat. Electron. 5, 672–681 (2022).

    Article 

    Google Scholar
     

  • Howard, A. G. Mobilenets: efficient convolutional neural networks for mobile vision applications. Preprint at https://arxiv.org/abs/1704.04861 (2017).

  • Iandola, F. N. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. Preprint at https://arxiv.org/abs/1602.07360 (2016).

  • Xu, C. et al. Depth-based subgraph convolutional neural networks. In 2018 24th International Conference on Pattern Recognition (ICPR) 1024–1029 (IEEE, 2018).

  • Chollet, F. Xception: deep learning with depthwise separable convolutions. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1251–1258 (IEEE, 2017).

  • Lin, M., Chen, Q., & Yan, S. Network in network. In 2nd International Conference on Learning Representations (ICLR, 2014).

  • Krizhevsky, A., Sutskever, I. & Hinton, G. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).

    Article 

    Google Scholar
     

  • Jacob, B. et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2704–2713 (IEEE, 2018).

  • Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R. & Bengio, Y. Quantized neural networks: training neural networks with low precision weights and activations. J. Mach. Learn. Res. 18, 187 (2018).

  • Bergstra, J., Bardenet, R., Bengio, Y. & Kégl, B. Algorithms for hyper-parameter optimization. Adv. Neural Inf. Process. Syst. 24, 2546–2554 (2011).


    Google Scholar
     

  • Absel, K., Manuel, L. & Kavitha, R. Low-power dual dynamic node pulsed hybrid flip-flop featuring efficient embedded logic. IEEE Trans. VLSI Syst. 21, 1693–1704 (2013).

    Article 

    Google Scholar
     

  • Celiker, H., Sou, A., Cobb, B., Dehaene, W. & Myny, K. Flex6502: a flexible 8b microprocessor in 0.8mum metal-oxide thin-film transistor technology implemented with a complete digital design flow running complex assembly code. In IEEE International Solid-State Circuits Conference (ISSCC) 272–274 (IEEE, 2022).

  • Myny, K. et al. An 8-bit, 40-instructions-per-second organic microprocessor on plastic foil. IEEE J. Solid State Circuit 47, 284–291 (2012).

    Article 
    ADS 

    Google Scholar
     

  • Bleier, N. et al. FlexiCores: low footprint, high yield, field reprogrammable flexible microprocessors. In 49th IEEE/ACM Annual International Symposium on Computer Architecture (ISCA) 831–846 (IEEE, 2022).

  • Myny, K. et al. 8b thin-film microprocessor using a hybrid oxide-organic complementary technology with inkjet-printed P2ROM memory. In IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC) 486–487 (IEEE, 2014).

  • Ozer, E. et al. Binary neural network as a flexible integrated circuit for odour classification. In 2nd IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) 1–4 (IEEE, 2020).

  • Shen, P. A flexible all-digital compute-in-memory AI chip for edge-computing. Zenodo https://doi.org/10.5281/zenodo.14268923 (2024).

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