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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\u2010gate (SG) Fe\u2010TFT reliability. SG Fe\u2010TFTs have limitations such as read\u2010disturbance and small memory windows, constraining their use. To mitigate these, dual\u2010gate (DG) Fe\u2010TFTs are modeled using technology computer\u2010aided design, comparing their performance. Compute\u2010in\u2010memory (CIM) architectures with SG and DG Fe\u2010TFTs are investigated for deep neural networks (DNN) accelerators, revealing heat's detrimental effect on reliability and inference accuracy. DG Fe\u2010TFTs exhibit about 4.6x higher throughput than SG Fe\u2010TFTs. 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\u2010TFTs, respectively. 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