Design of a Stable Low Power 11-T Static Random Access Memory Cell

In this paper, a 11-T static random-access memory (SRAM) cell has been examined that shows a fair reduction in read power dissipation while upholding the stability and moderate performance. In the presented work, parametric variability analysis of various design metrices such as signal to noise margin, read current and read power of the Proposed 11T cell are presented and compared with few considered topologies. The Proposed cell offers single ended write operation and differential read operation. The improvement in read signal to noise margin and write signal to noise margin with respect to conventional 6T SRAM is 10.63% and 33.09%, respectively even when the write operation is single ended. Mean hold static noise margin of the cell for 3000 samples is [Formula: see text] times higher than considered D2p11T cell. Sensitivity analysis of data retention voltage (DRV) with respect to temperature variations is also investigated and compared with considered topologies. DRV variation with temperature is least in FF process corner. In comparison to conventional 6T SRAM cell, the write and read delay of Proposed 11T cell gets improved by [Formula: see text] and 1.64%, respectively. Proposed 11T topology consumes least read energy in comparison with considered topologies. In comparison with another considered 11T topology, i.e., D2p11T cell, Proposed cell consumes 13.11% lesser area. Process variation tolerance with Monte Carlo simulation for read current and read power has been investigated using Cadence virtuoso tool with GPDK 45-nm technology.

Demultiplexing barcoded Oxford Nanopore reads with deep convolutional neural networks

Multiplexing, the simultaneous sequencing of multiple barcoded DNA samples on a single flow cell, has made Oxford Nanopore sequencing cost-effective for small genomes. However, it depends on the ability to sort the resulting sequencing reads by barcode, and current demultiplexing tools fail to classify many reads. Here we present Deepbinner, a tool for Oxford Nanopore demultiplexing that uses a deep neural network to classify reads based on the raw electrical read signal. This ‘signal-space’ approach allows for greater accuracy than existing ‘base-space’ tools (Albacore and Porechop) for which signals must first be converted to DNA base calls, itself a complex problem that can introduce noise into the barcode sequence. To assess Deepbinner and existing tools, we performed multiplex sequencing on 12 amplicons chosen for their distinguishability. This allowed us to establish a ground truth classification for each read based on internal sequence alone. Deepbinner had the lowest rate of unclassified reads (7.8%) and the highest demultiplexing precision (98.5% of classified reads were correctly assigned). It can be used alone (to maximise the number of classified reads) or in conjunction with other demultiplexers (to maximise precision and minimise false positive classifications). We also found cross-sample chimeric reads (0.3%) and evidence of barcode switching (0.3%) in our dataset, which likely arise during library preparation and may be detrimental for quantitative studies that use multiplexing. Deepbinner is open source (GPLv3) and available at https://github.com/rrwick/Deepbinner.

Thermal Asperities Sensitivity to Particles: Methodology and Test Results

Thermal asperities (TA’s) are tribological events that cause repercussions for giant magnetoresistance (GMR) and MR heads in the hard disk drive industry. A TA is a read signal spike caused by sensor temperature rise due to contact with disk asperities or contaminant particles. TA events may cause GMR and MR heads to temporarily lose their reading capability, and may potentially damage the transducer. It is difficult to completely avoid particle contamination in hard drive applications. Hence it is necessary to design heads/media with a minimum TA sensitivity to particles. A test method for TA sensitivity to particles is needed. This work developed a test method for TA sensitivities to particles. The test system includes a CSS tester with TA detection capability, a chamber to contain the head/disk interface, a particle atomizer, and a particle counter. Aluminum silicate particles used in the test have sizes ranging from 0.2 to 1.0 μm. Particles are injected into the chamber during head scan for TA’s from ID to OD with adjustable air-borne concentrations in the chamber from 10×106 to 30×106particles/m3. TA counts of 30 scans are averaged to obtain reliable TA sensitivity data. Media with different lubricant thickness, different carbon overcoats, and different lubricant types are tested with this method. The results indicated that this methodology can effectively differentiate TA sensitivity to particles for the media studied.