A mixed-signal feed-forward neural network architecture with on-chip learning in CMOS 0.18 microns.

One of the main characteristics of the neural networks is their high number of interconnections between the neurons through synaptic multipliers. Interconnections occupy large area and increase the circuit complexity which limits the size of the fully parallel network. To implement large size networks, time-multiplexing should be used. Two new mixed-signal time-multiplexed architectures are proposed for on-chip mixed-signal neural networks. MRIII is used for training the network which is more robust to mixed-signal designs. The problem of node addressing and routing is solved by performing the operations in current mode. The architectures are simple and compact and learning is performed on-chip without the host computer, which reduces the cost of learning for the network. Mixed-signal MDACs are used for synaptic multiplication. A new compact architecture is proposed for the MDAC to reduce the area, power consumption and noise. The proposed MDAC performs the digital to analog conversion in series. Comparison shows that the new MDAC is more linear and has less noise than the conventional MDAC. The layout of the proposed MDAC is relatively easy, since it has a repetitive structure. For the first time, a new 12-bit MDAC is implemented, which enables us to perform on-chip training. The proposed 12-bit MDAC still occupies less area compared to the 7-bit conventional MDAC. A new low-voltage class-AB high-drive buffer for driving the voltages off-chip is developed. The proposed buffer is able to drive capacitive loads up to 2nF. It also drives resistive loads down to 2kO from rail to rail. For compensation, a 0.2pF capacitor is used.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .M47. Source: Masters Abstracts International, Volume: 42-01, page: 0298. Adviser: M. Ahmadi. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003

Positive feedback technique and split‐length transistors for DC‐gain enhancement of two‐stage op‐amps

This study presents the design and simulation of a fully differential two‐stage op‐amp in a 0.18 μm complementary metal–oxide–semiconductor process with a 1.8 V supply voltage. In this op‐amp, positive feedback technique and split‐length transistors (SLTs) are employed to increase the DC‐gain of the op‐amp by about 22 dB without affecting the unity‐gain bandwidth (UGBW), stability, power dissipation and output voltage swing of the conventional two‐stage op‐amp. A comprehensive analysis is provided for differential‐mode gain, common‐mode gain, power supply rejection ratio, input‐referred noise, input offset, frequency response and the effect of using SLTs on DC‐gain sensitivity. The proposed op‐amp is utilised in a flip‐around sample‐and‐hold amplifier (SHA). The output spectrum of the SHA shows the total harmonic distortion of 0.0023%. The post‐layout and Monte Carlo simulation results show that the proposed op‐amp has better performance than the state‐of‐the‐art designs

Design of a Real-Time Corrosion Detection and Quantification Protocol for Automobiles

Corrosion can compromise the integrity of the vehicle. Instead, “rust proofing” a vehicle can prolong its usable life span, reducing material waste overall and permitting greater salvageability at the end of the vehicle’s life. For rust proofing, a definitive and consistent approach for detecting corrosion could be beneficial. Instead, most vehicle corrosion detection and assessment is performed visually and in an ad hoc manner without following any particular guidelines. The visual examination of corrosion depends highly on the method of analyzing and interpreting the corrosion, as well as operator’s experience in assessing and applying rust proofing. As a result, any visual assessment strategy needs standardization to minimize human error. An automated method is proposed to identify and analyze surface rust and appraise its severity for vehicles. The method demonstrated is 96% effective, low-cost, and has low computational complexity. Subsequently, the method has the potential to be conveyed to different advanced devices, such as smartphones, to measure corrosion, decreasing errors and improving measurement accuracy. Low implementation cost, and high reliability of the method contributes to its ease of use in the field, and hence, advances its accessibility to automotive professionals to identify and monitor corrosion levels, without the interference of human errors

Building a Versatile Platform for the Detection of Protein–Protein Interactions Based on Organic Field-Effect Transistors

Detection and characterization of biomolecular interactions, such as protein–protein interactions (PPIs), are critical to a fundamental understanding of biochemical processes, thus being a driver of innovation for drug discovery, clinical diagnostics, and protein engineering. Among the many sensor types used to probe PPIs, organic field-effect transistors are particularly desirable due to their unique features, including tunability, sensitivity, low-power requirements, and multi-parameter readouts. This work describes the development of a biosensor based on organic field-effect transistors, covalently functionalized at the surface with an engineered ubiquitin variant for the specific and sensitive detection of ubiquitin-specific protease 8 (USP8). The resulting sensor was carefully characterized to reveal both electronic and solid-state properties. The sensing platform showed high sensitivity (sub-nanomolar analyte concentrations) and selectivity for USP8 and robust performance that suggests that it may be highly tunable. The sensing system introduced in this work provides a detection method for PPIs, which constitutes a promising platform for advanced biotechnology applications