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This paper introduces an 8T single-ended SRAM cell to improve stability and decrease energy consumption. It cuts the pull-down path to the storage node written '1', enhancing the write ability. It uses an isolated read path to enhance the read stability with low power and energy consumption. At a supply voltage of 0.5 V, the read static noise margin, write margin, read energy, and write energy of the proposed cell are superior by 320%, 233%, 12%, and 26%, respectively, compared to the conventional 6T. We performed 5,000 Monte Carlo simulations in the 32 nm technology to evaluate read yields. The results show that our cell has 6.9x more yields than the conventional 6T SRAM cell. Therefore, the proposed cell can be a good option for high stability and low power/energy applications. [ABSTRACT FROM AUTHOR]

Conventional static random‐access memory (SRAM) suffers from high leakage power in advanced complementary metal‐oxide‐semiconductor nodes. Meanwhile, gain‐cell embedded dynamic random‐access memory (GC‐eDRAM) is an area‐efficient alternative to SRAM, although it requires periodic refresh cycles. In this regard, this study proposes a fin field‐effect transistor (FinFET)‐based 5T GC‐eDRAM bitcell that addresses the leakage power issue of SRAM while avoiding the bandwidth‐consuming refreshes of GC‐eDRAM. Furthermore, for the feasibility of scaling down transistors, the gain‐cell design is based on the FinFET, which overcomes short‐channel problems. The proposed 5T bitcell provides additional internal feedback relative to the 4T all‐nMOS fully depleted‐silicon on insulator (FD‐SOI) gain cell to ensure the static retention of both "1" and "0" data. The 2‐kB array was simulated by employing a 7‐nm FinFET predictive technology model (PTM) using HSPICE. The simulation results show that the proposed 5T bitcell (at 0.7 V) enables over 13× smaller data retention power and 10× area reduction compared to the 4T all‐nMOS GC‐eDRAM cell in a 28‐nm FD‐SOI technology. [ABSTRACT FROM AUTHOR]

In this paper, we propose a high-precision memristive neural network with neurons implemented by complementary metal oxide semiconductor (CMOS) inverters. Regarding the process variations in the memristors and the sensitivity of the memristive crossbar structure to these fluctuations, the read operation with repetitive pulses and feedback-based write in the memristors are used to implement the neural networks trained by the ex-situ method. Moreover, accurate modeling of the neuron circuit (CMOS inverter) and decreasing the mismatch between trained weights and the limited memristances fill the gap between simulation and implementation. To employ physical constraints based on the memristor framework during the training phase, a linear function is utilized to map the trained weights to the acceptable range of memristances after the training phase. To solve the vanishing gradient problem due to the use of the tanh function as an activation function and for better learning of the network, some measures are taken. Moreover, fin field-effect transistor (FinFET) technology is used to prevent the reduction of the accuracy of the inverter-based memristive neural networks due to the process variations. Overall, our implementation improves the speed, area, power-delay product (PDP), and mean square error (MSE) of the training stage by 91.43%, 95.06%, 48.29% and 81.64%, respectively. [ABSTRACT FROM AUTHOR]