Thermal Effects on Monolithic 3D Ferroelectric Transistors for Deep Neural Networks Performance

Abstract

Monolithic three-dimensional (M3D) integration advances integrated circuits by enhancing density and energy efficiency. Ferroelectric thin-film transistors (Fe-TFTs) attract attention for neuromorphic computing and back-end-of-the-line (BEOL) compatibility. However, M3D faces challenges like increased runtime temperatures due to limited heat dissipation, impacting system reliability. This work demonstrates the effect of temperature impact on single-gate (SG) Fe-TFT reliability. SG Fe-TFTs have limitations such as read-disturbance and small memory windows, constraining their use. To mitigate these, dual-gate (DG) Fe-TFTs are modeled using technology computer-aided design, comparing their performance. Compute-in-memory (CIM) architectures with SG and DG Fe-TFTs are investigated for deep neural networks (DNN) accelerators, revealing heat's detrimental effect on reliability and inference accuracy. DG Fe-TFTs exhibit about 4.6x higher throughput than SG Fe-TFTs. Additionally, thermal effects within the simulated M3D architecture are analyzed, noting reduced DNN accuracy to 81.11% and 67.85% for SG and DG Fe-TFTs, respectively. Furthermore, various cooling methods and their impact on CIM system temperature are demonstrated, offering insights for efficient thermal management strategies.