In this paper, a novel radiation-hardened 14-transistor SRAM bitcell with speed and power optimized [radiation-hardened with speed and power optimized (RSP)-14T] for space application is proposed. By circuit- and layout-level optimization design in a 65-nm CMOS technology, the 3-D TCAD mixed-mode simulation results show that the novel structure is provided with increased resilience to single-event upset as well as single-event-multiple-node upsets due to the charge sharing among off-transistors. Moreover, the HSPICE simulation results show that the write speed and power consumption of the proposed RSP-14T are improved by ~65% and ~50%, respectively, compared with those of the radiation hardened design (RHD)-12T memory cell.

In-memory computing establishes a new and promising computing paradigm aimed at solving problems caused by the von Neumann bottleneck. It eliminates the need for frequent data transfer between the memory and processing modules and enables the parallel activation of multiple lines. However, vertical data storage is generally required, increasing the implementation complexity for the SRAM writing mode. This article proposes a 10-transistor (10T) SRAM to omit vertical data storage and improve the stability of in-memory computing. A cross-layout of the word line enables arrays with multirow or multicolumn parallel activation to perform vector logic operations in two directions. In addition, the novel horizontal read channel allows matrix transposition. By reconfiguring the data lines, sense amplifiers, and multiplexing read ports, the proposed SRAM can be regarded as a content-addressable memory (CAM), and its symmetry provides selectable data search by column or by row according to the application that easily fits the SRAM storage mode without additional data adjustments. A proposed self-termination structure aims to decrease search energy consumption by approximately 38.5% at 0.9 V at the TT process corner. To verify the effectiveness of the proposed design, a 4 Kb SRAM was implemented in 28-nm CMOS technology. The read margin of the proposed 10T SRAM cell is three times higher than that of the conventional 6-transistor cell. At 0.9 V, logic operations can be performed at approximately 300 MHz, and binary CAM search operations are achieved at approximately 260 MHz with around 1 fJ of energy consumption per search/bit.

In this brief, we proposed, based on the polarity upset mechanism of single-event transient voltage of n-channel metal-oxide-semiconductor (nMOS) transistors, a novel radiation hardened by polar design (RHPD) 12T SRAM cell to enhance the reliability and operation speed for space applications. Simulation results in Semiconductor Manufacturing International Corporation (SMIC) 65-nm CMOS commercial standard process show that the proposed RHPD-12T cell can tolerate all single-node upsets. Meanwhile, compared with We-QUATRO, QUATRO, and dual interlocked storage cell (DICE), the write speed of the proposed cell can be reduced by ~41.8 and ~35.3%, and the static power consumption is reduced by ~41.6 and ~46.3%, respectively. Monte Carlo (MC) simulation has proved that under high frequency and low supply (0.6 V) voltage, RHPD-12T has the minimum write failure probability compared with five other SRAM cells.

The von Neumann architecture is approaching its limits in terms of scalability and power consumption. In-memory computation is a possible approach to mitigate this limitation. This brief proposes a configurable 8T static random access memory (SRAM) cell with double word lines and three read ports for in-memory computing. In addition to the normal SRAM function, xor/xnor and compound Boolean logic operations of three or four operands, such as and-or, and-or-invert, or-and, and or-and-invert, can be performed in one cycle by fully utilizing the three read ports to obtain 13.2-fJ/bit consumption at 0.6 V. The logic operation frequency is 793 MHz at 1.2 V. The proposed SRAM effectively resolves the bottleneck of the existing in-memory computation schemes that only support compound Boolean logic operations with more than two cycles. In addition, the proposed SRAM array scheme can be configured and used as a binary content-addressable memory or a ternary content-addressable memory for searching operations; it achieves 0.24 fJ/search/bit at 0.6 V in the worst case. At 1.2 V, the searching frequency is up to 813 MHz when searching 128 bits with 65-nm technology.

Although multirow read is essential to achieve static random access memory (SRAM) in-memory computing (IMC), it may undermine circuit linearity and computational consistency across columns. In this study, we investigated the causes of nonlinearity and inconsistency. Based on detailed analyses, we proposed a cascade current mirror (CCM) peripheral circuit. Only four transistors were added to each bitline (BL) for voltage clamping and proportionally mirroring the read current. In addition, a 6T SRAM cell with double word lines operating with the CCM further reduced the delay and improved the computational consistency. We applied the structure to numerous prior studies and evaluated them using the 28-nm complementary metal-oxide semiconductor process. The measurement results show that the proposed CCM can reduce the integer nonlinearity by up to 70% at 0.8-V supply, and the computational consistency is substantially improved by 56.84% at 0.9-V supply. In addition, we verified the performance improvement through classification using a convolutional neural network, achieving 91% accuracy in the MNIST and 86% accuracy in the CIFAR-10. The area overhead was 1.77% in a 512×512 SRAM array when integrating the proposed CCM circuit.

Speech recognition involves the ability to learn the audios which are closely related to event sequence. Although speech recognition has been widely implemented in software neural networks, a hardware implementation based on energy efficient computing architecture is still missing. Herein, W/MgO/SiO₂/Mo memristor arrays with multilevel resistance states are fabricated, where the weights of the artificial synapses in the memristor array can be tuned precisely by voltage pulses. Based on the array, speech recognition in memristive spiking neural networks (SNNs) with improved supervised tempotron algorithm on Texas Instruments digit sequences (TIDIGITS) dataset is conducted, demonstrating software‐comparable accuracy for speech recognition in the memristive SNN. It is envisioned that such memristive SNNs can pave the way to building a bioinspired spike‐based neuromorphic system for audio learning.