Flexible electronics can create lightweight, conformable components that could be integrated into smart systems for applications in healthcare, wearable devices and the Internet of Things. Such integrated smart systems will require a flexible processing engine to address their computational needs. However, the flexible processors demonstrated so far are typically fabricated using low-temperature poly-silicon thin-film transistor (TFT) technology, which has a high manufacturing cost, and the processors that have been created with low-cost metal-oxide TFT technology have limited computational capabilities. Here, we report a processing engine that is fabricated with a commercial 0.8-μm metal-oxide TFT technology. We develop a resource-efficient machine learning algorithm (the ‘univariate Bayes feature voting classifier’) and demonstrate its implementation with hardwired parameters as a flexible processing engine for an odour recognition application. Our flexible processing engine contains around 1,000 logic gates and has a gate density per area that is 20–45 times higher than other digital integrated circuits built with metal-oxide TFTs.
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The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.
The code used to generate the plots within this paper is available from the corresponding author upon reasonable request.
OE-A Roadmap for Organic and Printed Electronics White Paper 8th Edn (OE-A, 2020).
Nathan, A. et al. Flexible electronics: the next ubiquitous platform. Proc. IEEE 100, 1486–1517 (2012).
Kelly, P. H. J. Architecture and software for when there’s no longer plenty of room at the bottom. In Report from Dagstuhl Seminar 17061 (Eds. Castrillón-Mazo, J. et al.) https://doi.org/10.4230/DagRep.7.2.1 (Dagstuhl, 2017).
Lee, E. A. Programmable DSP architectures: part I. IEEE ASSP Mag. 5, 4–19 (1988).
Fisher, J. A., Faraboschi, P. & Desoli, G. Custom-fit processors: letting applications define architectures. In Proc. 29th Annual IEEE/ACM Int. Symposium on Microarchitecture (MICRO-29) 324–335 (IEEE, 1996).
Hennessy, J. L. & Patterson, D. A. A new golden age for computer architecture. Commun. ACM 62, 48–60 (2019).
Flex-ICs: Silicon-on-Polymer Products (American Semiconductor, 2020); https://www.americansemi.com/flex-ics.html
Gupta, S., Navaraj, W. T., Lorenzelli, L. & Dahiya, R. Ultra-thin chips for high-performance flexible electronics. npj Flex. Electron. 2, 8 (2018).
Harendt, C. et al. Hybrid systems in foil (HySiF) exploiting ultra-thin flexible chips. In 44th European Solid-State Device Research Conference (ESSDERC) 210–213 (IEEE, 2014).
Khan, S., Lorenzelli, L. & Dahiya, R. Technologies for printing sensors and electronics over large flexible substrates: a review. IEEE Sens. J. 15, 3164–3185 (2015).
Takayama, T. et al. A CPU on a plastic film substrate. In Symposium on VLSI Technology 230–231 (IEEE, 2004).
Dembo, H. et al. RFCPUs on glass and plastic substrates fabricated by TFT transfer technology. In IEEE Int. Electron Devices Meeting (IEDM) 125–127 (IEEE, 2005).
Karaki, N. et al. A flexible 8 b asynchronous microprocessor based on low-temperature poly-silicon TFT technology. In IEEE Int. Solid-State Circuits Conf. (ISSCC) 272–273 (IEEE, 2005).
Kurokawa, Y. et al. UHF RFCPUs on flexible and glass substrates for secure RFID systems. IEEE J. Solid State Circ. 43, 292–299 (2008).
Hills, G. et al. Modern microprocessor built from complementary carbon nanotube transistors. Nature 572, 595–602 (2019).
Petti, L. et al. Metal oxide semiconductor thin-film transistors for flexible electronics. Appl. Phys. Rev. 3, 021303 (2016).
Myny, K. The development of flexible integrated circuits based on thin-film transistors. Nat. Electron. 1, 30–39 (2018).
Myny, K., van Veenendaal, E., Gelinck, G. H., Genoe, J. & Dehaene, W. An 8-bit, 40-instructions-per-second organic microprocessor on plastic foil. IEEE J. Solid State Circ. 47, 284–291 (2012).
Myny, K. et al. 8 b thin-film microprocessor using a hybrid oxide-organic complementary technology with inkjet-printed P2ROM memory. In IEEE Int. Solid-State Circuits Conf. (ISSCC) 486–487 (IEEE, 2014).
FlexLogIC (PragmatIC, 2020); https://www.pragmatic.tech/create-more/devices
Torsi, L., Magliulo, M., Manoli, K. & Palazzo, G. Organic field-effect transistor sensors: a tutorial review. Chem. Soc. Rev. 42, 8612–8628 (2013).
Tate, D. J. et al. Fully solution processed low voltage OFET platform for vapour sensing applications. In ISOCS/IEEE Int. Symposium on Olfaction and Electronic Nose 1–3 (IEEE, 2017).
Rahmanudin, A. et al. Robust high‐capacitance polymer gate dielectrics for stable low‐voltage organic field‐effect transistor sensors. Adv. Electron. Mater. 6, 1901127 (2020).
Ozer, E. et al. Bespoke machine learning processor development framework on flexible substrates. In IEEE Int. Conf. Flexible and Printable Sensors and Systems (FLEPS) 1–3 (IEEE, 2019).
Myny, K. et al. A flexible ISO14443-A compliant 7.5-mW 128 b metal-oxide NFC barcode tag with direct clock division circuit from 13.56-MHz carrier. In IEEE Int. Solid-State Circuits Conf. (ISSCC) 258–259 (IEEE, 2017).
This work is partially supported by Innovate UK through the ‘PlasticARMPit: Accelerating the Development of Flexible Integrated Smart Systems (no. 103390)’ project.
The authors declare no competing interests.
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Forward transfer characteristic of a metal-oxide TFT.
The column on the left shows the list of input, intermediate and output signals. Sensor[4:0] and Address[2:0] are the inputs, and represent the 5-bit sensor data, and 3-bit sensor address, respectively. SensorX_vote[4:0] is intermediate signals, and represent the 5-bit BC coefficients (essentially votes) for each sensor. Finally, Output[4:0] shows the 5-bit one-hot predicted class as output.
The top row shows the sensor data values from 0 to 31. For each sensor value, the BC or vote of the sensor is predetermined and hardwired in the microarchitecture.
Extended Data Fig. 4 NFPE chip measurement results of a fabricated chip for the same set-up as in the simulation.
This is the waveform captured from the logic analyser. All inputs and outputs are shown as individual signals. Sensor_X and Address_X are input signals, and represent the 5-bit sensor data and 3-bit address. Output_X represents the 5-bit one-hot predicted class output signals.
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Ozer, E., Kufel, J., Myers, J. 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). https://doi.org/10.1038/s41928-020-0437-5
Nature Electronics (2020)