This paper introduces a CMOS operational amplifier with rail-to-rail input and output voltage ranges, suitable for operation in extremely low-voltage environments. The approach is based on a bulk-driven input stage with extended input common-mode voltage range, in which the effective input transconductance is enhanced by means of a partial positive feedback loop. As a result, a gain and gain-bandwidth product performance similar to that of an amplifier using a gate-driven approach is obtained. Output rail-to-rail operation is achieved by means of a push-pull stage, which is biased in class-AB by using a static feedback loop, thus avoiding frequency limitations inherent in dynamic-feedback tuning schemes. The proposed two-stage operational amplifier was designed to operate with a 1-V supply, and a test chip prototype was fabricated in 0.35-mum standard CMOS technology. The experimental performance features an open-loop DC gain higher than 76 dB and a closed-loop unity-gain bandwidth above 8 MHz when a 1-MOmegapar17-pF load is connected to the amplifier output.

The constraints on the design of CMOS operational amplifiers with rail-to-rail input range for extremely low supply voltage operation, are addressed. Two design approaches for amplifiers based on complementary input differential pairs and a single input pair, respectively, are presented. The first realizes a feedforward action to accommodate the common-mode (CM) component of the input signals to the amplifier input range. The second approach performs a negative feedback action over the input CM signal. Two operational amplifiers based on the proposed approaches have been designed for 1-V total supply operation, and fabricated in a standard 1.2-/spl mu/m CMOS process. Experimental results are provided and the corresponding performances are discussed and compared.

Cork is a natural material produced in the Mediterranean countries. Cork stoppers are used to seal wine bottles, Cork stopper quality classification is a practical pattern classification problem. The cork stoppers are grouped into eight classes according to the degree of defects on the cork surface. These defects appear in the form of random-shaped holes, cracks, and others. As a result, the classification cork stopper is not a simple object recognition problem. This is because the pattern features are not specifically defined to a particular shape or size. Thus, a complex classification form is involved, Furthermore, there is a need to build a standard quality control system in order to reduce the classification problems in the cork stopper industry. The solution requires factory automation meeting low time and reduced cost requirements. This paper describes a cork stopper quality classification system using morphological filtering and contour extraction and following (CEF) as the feature extraction method, and a fuzzy-neural network as a classifier. This approach will be used on a daily basis. A new adaptive image thresholding method using iterative and localized scheme is also proposed, A fully functioning prototype of the system has been built and successfully tested. The test results showed a 6.7% rejection ratio, It is compared with the 40% counterpart provided by traditional systems. The human experts in the cork stopper industry rated this proposed classification approach as excellent.