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.

Keywords

• BEOL
• deep neural network
• ferroelectric
• monolithic 3D
• thin‐film transistor