Search Results (19)

Low-cost, smartphone-based thermal cameras offer unprecedented accessibility for physiological monitoring, yet their validity and reliability for absolute skin temperature measurement in clinical settings remain contentious. This study aims to quantify the agreement and repeatability of a widely used smartphone thermal camera, the FLIR One Pro, against a consumer-grade, non-contact infrared thermometer, the iHealth PT3. A method comparison study was conducted with 40 healthy adult participants, yielding a total of 2400 temperature measurements. Skin temperature of the hand dorsum was measured concurrently with the FLIR One Pro and the iHealth PT3. The protocol involved two rounds: Round 1 (R1) in a stable, static environment to assess baseline repeatability, and Round 2 (R2) in a dynamic environment mimicking clinical repositioning. The performance of the instruments was compared using paired t-tests for mean differences and Bland–Altman analysis for assessing agreement. The iHealth PT3 demonstrated superior precision, with an average intra-participant standard deviation (SD) of 0.030 °C in R1 and 0.092 °C in R2. In stark contrast, the FLIR One Pro exhibited significantly higher variability, with an average SD of 0.34 °C in R1 and 0.30 °C in R2. Bland–Altman analysis revealed a substantial mean bias of −1.42 °C in R1 and −1.15 °C, with critically wide 95% limits of agreement ranges of ≈6 °C. The substantial systematic bias and poor agreement of the FLIR One Pro far exceed both its manufacturer-stated accuracy and clinically acceptable error margins for absolute temperature measurement. To further examine whether calibration could mitigate these deficiencies, we applied a suite of ten machine learning regressors to map FLIR readings onto iHealth PT3 values. Calibration reduced systematic bias across all models, with Quantile Gradient-Boosted Regression Trees achieving the lowest MAE (1.162 °C). The Extra Trees model yielded the lowest RMSE (1.792 °C) and the highest explained variance ($R^2$ = 0.152), yet this relatively low value confirms that the device’s high intrinsic variability limits the effectiveness of algorithmic correction. As such the device has limited utility for longitudinal patient monitoring or for diagnostic decisions that rely on precise, absolute temperature thresholds. These findings inform medical practitioners in low-resource settings of the profound limitations of using this device as a standalone clinical thermometer and emphasize that algorithmic correction cannot compensate for fundamental hardware and measurement noise constraints. Full article

In this paper, a coupled negative bias temperature instability (NBTI)/hot carrier degradation (HCD) failure model is proposed on the 2-D voltage plane for aging simulation of SRAM circuits. According to the physical mechanism of failure, based on the reaction–diffusion and hot carrier energy-driven theory, revised degradation models of threshold voltage shift (∆Vth) for the NBTI and HCD are established, respectively, with explicit expressions for gate voltage (V_G)/drain voltage (V_D). An NBTI/HCD coupling model is built on the 2-D {V_G, V_D} voltage plane with a weighting factor in the form of V_G and V_D power law. The model also takes into account the AC effect and long-term saturation behavior. The predicted ∆Vth under various stress conditions shows an average relative error of 11.6% with experimental data across the entire bias space. SRAM circuit simulation shows that the read static noise margin (RSNM) and write static noise margin (WSNM) have a maximum absolute error of 4.2% and 3.1%, respectively. This research provides a valuable reference for the reliability simulation of nanoscale integrated circuits. Full article

With continued CMOS scaling, transistor miniaturization has significantly raised SRAM integration density while lowering the critical charge (Qc), increasing cell vulnerability to spaceborne high-energy particles. Single-event upset (SEU) and especially single-event multiple node upsets (SEMNU) due to charge sharing present major reliability challenges. To overcome these issues, this study introduces a radiation-hardened 20T SRAM cell with read/write optimization (RWO-20T) designed for space applications. Benchmarking against hardened cells RH14T, RHSCC16T, S8P8N16T, and CC18T reveals that RWO-20T delivers superior read static noise margin (RSNM), increased word-line write trip voltage (WWTV), and faster read and write access times. Although the higher transistor count incurs some area overhead and slightly lowers the hold static noise margin (HSNM), RWO-20T achieves improved recovery rates for dual-node upsets (DNU) and triple-node upsets (TNU) under SEMNU conditions. The circuits were simulated in a 90 nm CMOS process and operated at 1 V. Full article

Electronic equipment in space is constantly exposed to high-energy particles, which can induce Single Event Upsets (SEUs) in memory components, threatening system reliability. To address this critical challenge, we propose RHLP-18T, a radiation-hardened 18-transistor (18T) Static Random-Access Memory (SRAM) cell designed to enhance robustness against radiation-induced faults. The proposed cell integrates circuit-level Radiation-Hardened-by-Design (RHBD) techniques to mitigate both SEUs and multi-node upsets. Comprehensive simulations were conducted using 90 nm CMOS technology, benchmarking RHLP-18T against nine existing RHBD cells (RHBD14T, HPHS12T, NRHC14T, QCCS12T, RHMC12T, RHWC12T, SEA14T, SIMR-18T, and SERSC16T). Simulation results demonstrate that the proposed RHLP-18T cell exhibits superior SEU tolerance, achieving a Read Static Noise Margin (RSNM) over three times higher than the next best design. Moreover, the proposed cell achieves the lowest hold power consumption among all evaluated cells. These improvements result in the highest Figure of Merit (FOM), indicating that RHLP-18T provides an optimal trade-off between robustness and overall performance for operation in radiation-exposed environments. Full article

The need for power-efficient devices, such as smart sensor nodes, mobile devices, and portable digital gadgets, is markedly increasing and these devices are becoming commonly used in daily life. These devices continue to demand an energy-efficient cache memory designed on Static Random-Access Memory (SRAM) with enhanced speed, performance, and stability to perform on-chip data processing and faster computations. This paper presents an energy-efficient and variability-resilient 11T (E²VR11T) SRAM cell, which is designed with a novel Data-Aware Read–Write Assist (DARWA) technique. The E²VR11T cell comprises 11 transistors and operates with single-ended read and dynamic differential write circuits. The simulated results in a 45 nm CMOS technology exhibit 71.63% and 58.77% lower read energy than ST9T and LP10T and lower write energies of 28.25% and 51.79% against S8T and LP10T cells, respectively. The leakage power is reduced by 56.32% and 40.90% compared to ST9T and LP10T cells. The read static noise margin (RSNM) is improved by 1.94× and 0.18×, while the write noise margin (WNM) is improved by 19.57% and 8.70% against C6T and S8T cells. The variability investigation using the Monte Carlo simulation on 5000 samples highly validates the robustness and variability resilience of the proposed cell. The improved overall performance of the proposed E²VR11T cell makes it suitable for low-power applications. Full article

We comprehensively investigate displacement-defect-induced current and static noise margin variations in six-transistor (6T) static random access memory (SRAM) based on a 10 nm node fin field-effect transistor (FinFET) using technology computer-aided design (TCAD). Various defect cluster conditions and fin structures are considered as variables to estimate the worst-case scenario for displacement defects. The rectangular defect clusters capture more widely distributed charges at the fin top, reducing the on- and off-current. The read static noise margin (RSNM) is the most degraded in the pull-down transistor during the read operation. The increased fin width decreases the RSNM due to the gate field. The current per cross-sectional area increases when the fin height decreases, but the energy barrier lowering by the gate field is similar. Therefore, the reduced fin width and increased fin height structure suit the 10 nm node FinFET 6T SRAMs with high radiation hardness. Full article

Combining the advantages of low-power consumption of static random access memory (SRAM) with high stability and nonvolatile of resistive memory (RRAM), an improved 8T2R nonvolatile SRAM (nvSRAM) memory cell was proposed in this paper. In order to solve the problem that data cannot be stored when SRAM is powered off, RRAM technology was introduced into SRAM to realize an SRAM with nonvolatile function. The differential mode was adopted to improve the data restoration speed. Meanwhile, a pre-decoding technology was proposed to realize fast address decoding, and a voltage-mode sensitive amplifier was used to achieve fast amplification of two bit lines, so as to improve the reading speed of the memory. An 8kb nvSRAM was implemented with a CMOS 28 nm 1P9M process. The simulation results show that when the power supply voltage was 0.9 V, the static/read/write noise margin was 0.35 V, 0.16 V and 0.41 V, respectively. The data storage time was 0.21 ns, and restoration time was 0.18 ns. The time for the whole system to read 1 bit of data was 5.2 ns. Full article

In this work, a hybrid-phase transition field-effects-transistor (hyper-FET) integrated with phase-transition materials (PTM) and a multi-nanosheet FET (mNS-FET) at the 3 nm technology node were analyzed at the device and circuit level. Through this, a benchmark was performed for presenting device design guidelines and for using ultra-low-power applications. We present an optimization flow considering hyper-FET characteristics at the device and circuit level, and analyze hyper-FET performance according to the phase transition time (TT) and baseline-FET off-leakage current (I_OFF) variations of the PTM. As a result of inverter ring oscillator (INV RO) circuit analysis, the optimized hyper-FET increases speed by +8.74% and reduces power consumption by −16.55%, with I_OFF = 5 nA of baseline-FET and PTM TT = 50 ps compared to the conventional mNS-FET in the ultra-low-power region. As a result of SRAM circuit analysis, the read static noise margin is improved by 43.9%, and static power is reduced by 58.6% in the near-threshold voltage region when the PTM is connected to the pull-down transistor source terminal of 6T SRAM for high density. This is achieved at 41% read current penalty. Full article

The ever-growing interest in cryogenic applications has prompted the investigation for energy-efficient and high-density memory technologies that are able to operate efficiently at extremely low temperatures. This work analyzes three appealing embedded memory technologies under cooling—from room temperature (300 K) down to cryogenic levels (77 K). As the temperature goes down to 77 K, six-transistor static random-access memory (6T-SRAM) presents slight improvements for static noise margin (SNM) during hold and read operations, while suffering from lower (−16%) write SNM. Gain-cell embedded DRAM (GC-eDRAM) shows significant benefits under these conditions, with read voltage margins and data retention time improved by about 2× and 900×, respectively. Non-volatile spin-transfer torque magnetic random access memory (STT-MRAM) based on single- or double-barrier magnetic tunnel junctions (MTJs) exhibit higher read voltage sensing margins (36% and 48%, respectively), at the cost of longer write access time (1.45× and 2.1×, respectively). The above characteristics make the considered memory technologies to be attractive candidates not only for high-performance computing, but also enable the possibility to bridge the gap from room-temperature to the realm of cryogenic applications that operate down to liquid helium temperatures and below. Full article

Aerospace equipages encounter potential radiation footprints through which soft errors occur in the memories onboard. Hence, robustness against radiation with reliability in memory cells is a crucial factor in aerospace electronic systems. This work proposes a novel Carbon nanotube field-effect transistor (CNTFET) in designing a robust memory cell to overcome these soft errors. Further, a petite driver circuit to test the SRAM cells which serve the purpose of precharge and sense amplifier, and has a reduction in threefold of transistor count is recommended. Additionally, analysis of robustness against radiation in varying memory cells is carried out using standard GPDK 90 nm, GPDK 45 nm, and 14 nm CNTFET. The reliability of memory cells depends on the critical charge of a device, and it is tested by striking an equivalent current charge of the cosmic ray’s linear energy transfer (LET) level. Also, the robustness of the memory cell is tested against the variation in process, voltage and temperature. Though CNTFET surges with high power consumption, it exhibits better noise margin and depleted access time. GPDK 45 nm has an average of 40% increase in SNM and 93% reduction of power compared to the 14 nm CNTFET with 96% of surge in write access time. Thus, the conventional MOSFET’s 45 nm node outperforms all the configurations in terms of static noise margin, power, and read delay which swaps with increased write access time. Full article

We have simulated a monolithic three-dimensional inverter (M3DINV) structure by considering the interfacial trap charges generated thermally during the monolithic three-dimensional integration process. We extracted the SPICE model parameters from M3DINV structures with two types of inter-layer dielectric thickness T_ILD (=10,100 nm) using the extracted interface trap charge distribution of the previous study. Logic circuits, such as inverters (INVs), ring oscillators (ROs), a 2 to 1 multiplexer (MUX), and D flip-flop and 6-transistor static random-access memory (6T SRAM) containing M3DINVs, were simulated using the extracted model parameters, and simulation results both with and without interface trap charges were compared. The extracted model parameters reflected current reduction, threshold voltage increase, and subthreshold swing (SS) degradation due to the interface trap charge. HSPICE simulation results of the fanout-3 (FO3) ring oscillator considering the interface trap charges showed a 20% reduction in frequency and a 30% increase in propagation delay compared to those without the interface trap charges. The propagation delays of the 2 × 1 MUX and D flip-flop with the interface trap charges were approximately 78.2 and 39.6% greater, respectively, than those without the interface trap charges. The retention static noise margin (SNM) of the SRAM increased by 16 mV (6.4%) and the read static noise margin (SNM) of SRAM decreased by 43 mV (35.8%) owing to the interface trap charge. The circuit simulation results revealed that the propagation delay increases owing to the interface trap charges. Therefore, it is necessary to fully consider the propagation delay of the logic circuit due to the generated interface trap charges when designing monolithic 3D integrated circuits. Full article

An innovative and stable PNN based 10-transistor (10T) static random-access memory (SRAM) architecture has been designed for low-power bit-cell operation and sub-threshold voltage applications. The proposed design belongs to the following features: (a) pulse control based read-assist circuit offers a dynamic read decoupling approach for eliminating the read interference; (b) we have utilized the write data-aware techniques to cut off the pull-down path; and (c) additional write current has enhanced the write capability during the operation. The proposed design not only solves the half-selected problems and increases the read static noise margin (RSNM) but also provides low leakage power performance. The designed architecture of 1-Kb SRAM macros (32 rows × 32 columns) has been implemented based on the TSMC-40 nm GP CMOS process technology. At 300 mV supply voltage and 10 MHz operating frequency, the read and write power consumption is 4.15 μW and 3.82 μW, while the average energy consumption is only 0.39 pJ. Full article

We propose a novel ultra-low-power, voltage-based compute-in-memory (CIM) design with a new single-ended 8T SRAM bit cell structure. Since the proposed SRAM bit cell uses a single bitline for CIM calculation with decoupled read and write operations, it supports a much higher energy efficiency. In addition, to separate read and write operations, the stack structure of the read unit minimizes leakage power consumption. Moreover, the proposed bit cell structure provides better read and write stability due to the isolated read path, write path and greater pull-up ratio. Compared to the state-of-the-art SRAM-CIM, our proposed SRAM-CIM does not require extra transistors for CIM vector-matrix multiplication. We implemented a 16 k (128 × 128) bit cell array for the computation of 128× neurons, and used 64× binary inputs (0 or 1) and 64 × 128 binary weights (−1 or +1) values for the binary neural networks (BNNs). Each row of the bit cell array corresponding to a single neuron consists of a total of 128 cells, 64× cells for dot-product and 64× replicas cells for ADC reference. Additionally, 64× replica cells consist of 32× cells for ADC reference and 32× cells for offset calibration. We used a row-by-row ADC for the quantized outputs of each neuron, which supports 1–7 bits of output for each neuron. The ADC uses the sweeping method using 32× duplicate bit cells, and the sweep cycle is set to $2^{N-1}+1$, where N is the number of output bits. The simulation is performed at room temperature (27 °C) using 45 nm technology via Synopsys Hspice, and all transistors in bitcells use the minimum size considering the area, power, and speed. The proposed SRAM-CIM has reduced power consumption for vector-matrix multiplication by 99.96% compared to the existing state-of-the-art SRAM-CIM. Furthermore, because of the decoupled reading unit from an internal node of latch, there is no feedback from the reading unit, with read static noise, and margin-free results. Full article

To incur the memory interface and faster access of static RAM for near-threshold operation, a stable local bit-line static random-access memory (SRAM) architecture has been proposed along with the low-voltage pre-charged and negative local bit-line (NLBL) scheme. In addition to the low-voltage pre-charged and NLBL scheme being operated by the write bit-line column to work out for the write half-select condition. The proposed local bit-line SRAM design reduces variations and enhances the read stability, the write capacity, prevents the bit-line leakage current, and the designed pre-charged circuit has achieved an optimal pre-charge voltage during the near-threshold operation. Compared to the conventional 6 T SRAM design, the optimal pre-charge voltage has been improved up to 15% for the read static noise margin (RSNM) and the write delay enriched up to 22% for the proposed NLBL SRAM design which is energy-efficient. At 400 mV supply voltage and 25 MHz operating frequency, the read and write energy consumption is 0.22 pJ and 0.23 pJ respectively. After comparing with the related works, the access average energy (AAE) is lower than in other works. The overall performance for the proposed local bit-line SRAM has achieved the highest figure of merit (FoM). The designed architecture has been implemented based on the 1-Kb SRAM macros and TSMC−40 nm GP process technology. Full article

Power consumption and data processing speed of integrated circuits (ICs) is an increasing concern in many emerging Artificial Intelligence (AI) applications, such as autonomous vehicles and Internet of Things (IoT). Existing state-of-the-art SRAM architectures for AI computing are highly accurate and can provide high throughput. However, these SRAMs have problems that they consume high power and occupy a large area to accommodate complex AI models. A carbon nanotube field-effect transistors (CNFET) device has been reported as a potential candidates for AI devices requiring ultra-low power and high-throughput due to their satisfactory carrier mobility and symmetrical, good subthreshold electrical performance. Based on the CNFET and FinFET device’s electrical performance, we propose novel ultra-low power and high-throughput 8T SRAMs to circumvent the power and the throughput issues in Artificial Intelligent (AI) computation for autonomous vehicles. We propose two types of novel 8T SRAMs, P-Latch N-Access (PLNA) 8T SRAM structure and single-ended (SE) 8T SRAM structure, and compare the performance with existing state-of-the-art 8T SRAM architectures in terms of power consumption and speed. In the SRAM circuits of the FinFET and CNFET, higher tube and fin numbers lead to higher operating speed. However, the large number of tubes and fins can lead to larger area and more power consumption. Therefore, we optimize the area by reducing the number of tubes and fins without compromising the memory circuit speed and power. Most importantly, the decoupled reading and writing of our new SRAMs cell offers better low-power operation due to the stacking of device in the reading part, as well as achieving better readability and writability, while offering read Static Noise Margin (SNM) free because of isolated reading path, writing path, and greater pull up ratio. In addition, the proposed 8T SRAMs show even better performance in delay and power when we combine them with the collaborated voltage sense amplifier and independent read component. The proposed PLNA 8T SRAM can save 96%, while the proposed SE 8T SRAM saves around 99% in writing power consumption compared with the existing state-of-the-art 8T SRAM in FinFET model, as well as 99% for writing operation in CNFET model. Full article