DTCO Exploration of NOR-Type IGZO FeFETs for Read-Dominated Memories
For memory designers, this work provides a DTCO analysis of IGZO FeFETs for read-dominated memories, highlighting scalability and performance but also revealing critical challenges that need to be addressed.
This work evaluates NOR-type IGZO FeFETs for read-centric AI inference, achieving 10-A SRAM-equivalent area (0.016 μm²) with 7-nm ground rules and sub-5 ns random access latency, while identifying sensing margin penalties from sneak current that require positive-Vt engineering.
InGaZnO (IGZO) channel FeFETs have attracted notable interest thanks to their advances in endurance. This work evaluates the viability of NOR-type IGZO FeFETs for readcentric AI inference workloads via design-technology cooptimization (DTCO). We demonstrate the cross-node bitcell footprint scalability of back-end-of-line (BEOL) IGZO FeFETs capable of delivering 10-A SRAM-equivalent area (0.016 um2) with 7-nm ground rules and reaching sub-5 ns random access latency despite writability challenges. We further identify the sensing margin penalty in NOR FeFET arrays arising from sneak current associated with the negative program-state Vt, which requires positive-Vt engineering in order to eliminate the unwanted negative voltage read inhibition - for example, by ferroelectric layer thinning. Last but not least, we elucidate the read margin implications on 3D FeNOR for storage-class memories (SCMs), with the 3D stacking density limited by additional sneak current from neighbor channel shunting.