Bending Resistant Multibit Memristor for Flexible Precision Inference Engine Application

This work reports 2-bits/cell hafnium oxide-based stacked resistive random access memory devices fabricated on flexible polyimide substrates for neuromorphic applications considering the high thermal budget. The ratio of low-resistance state current (
${I}_{ \mathrm{\scriptscriptstyle ON}}$
) to high-resistance state current (
${I}_{ \mathrm{\scriptscriptstyle OFF}}$
) or
${I}_{ \mathrm{\scriptscriptstyle ON}}/{I}_{ \mathrm{\scriptscriptstyle OFF}}$
for the fabricated devices was above
$1.4\times10$
³
with a low device-to-device variation at
$100 \boldsymbol {\mu }\text{A}$
current compliance. The mechanical stability over 10
⁴
bending cycles at a 5 mm bending radius and endurance over 10
⁶
WRITE cycles makes these devices suitable for online neural network training. The data retention capability over 10
⁴
s at 125°C also infuses these devices’ long-term inference capability. Furthermore, the performance of the devices has been verified for neuromorphic applications by system-level simulations with experimentally calibrated data. The system-level simulation reveals only a 2% loss in inference accuracy over ten years from the baseline.