BACKGROUND Current guidelines for prostate carcinoma screening rely primarily on the digital rectal examination (DRE) and prostate specific antigen (PSA). Well described patient risk factors for prostate carcinoma also include age, ethnicity, family history, and complexed PSA. However, due to the nonlinear relation of each of these variables with prostate carcinoma, it is difficult to predict reliably each patient's risk based on linear univariate analysis. The authors investigated a neural network to model the risk of prostate carcinoma by seven readily available clinical features. METHODS The database for the current study comprised 3268 men recently evaluated for the early detection of prostate carcinoma. The seven clinical features evaluated included age, race, family history, International Prostate Symptom Score (IPSS), DRE, and total and complexed PSA. Three hundred forty‐eight subjects in the dataset included men with determined prostate biopsy outcomes and for whom at least 6 of 7 features were available. The dataset was divided randomly into a training set (60%) and a test set (40%), with n1/n2 cross‐validation used to evaluate model accuracy, and was modeled with linear and quadratic discriminant function analysis and a neural computational system. After a model with acceptable goodness of fit was achieved, reverse regression analysis using Wilks's generalized likelihood ratio test was performed to evaluate the statistical significance of each input variable. RESULTS The receiving operating characteristic (ROC) area for the neural computational system in the test set was 0.825, whereas total PSA and complexed PSA alone had ROC areas of 0.678 and 0.697, respectively. The ROC area of logistic regression in the test set was 0.510, linear discriminant function analysis was 0.674, and quadratic discriminant function analysis was 0.011. All were significantly less than the ROC area of the neural computational model (all Ps < 0.002). Reverse regression based on Wilks's generalized likelihood ratio test demonstrated each input feature to be highly significant to the model (all Ps ≪ 0.000001). CONCLUSIONS The authors modeled a combination of well described patient risk factors for prostate carcinoma using a neural computational system with acceptable goodness of fit. They demonstrated that each of the seven variates on which the model was based was critically significant to model performance. The authors presented this model for clinical use and suggested that clinicians use it in deciding to perform prostate biopsy. Cancer 2003. © 2003 American Cancer Society.

Using strained SiGe on Si, the threshold voltage of high PMOS devices is reduced by as much as 300mV. The 80nm devices exhibit excellent short channel characteristics such as DIBL and GIDL. For the first time a dual channel scheme using standard activation anneal temperature is applied that allows La 2 O 3 capping in NMOS and SiGe channel in PMOS to achieve acceptable values of threshold voltage for high and metal gates for 32nm node and beyond. Introduction While excellent advances have been made in acheving low NMOS threshold voltage with high performance [1-3], a manufacturable low PMOS V T still remains a challenge. We demonstrate here the modulation of PMOS V T with substrate band gap rather than relying on metal work function alone. The device is then integrated into the PMOS region with La 2 O 3 capping in the NMOS region to attain symmetric CMOS devices on the same chip, both with high performance. DiscussionThe issues with achieving proper valence band-edge work function for high /metal gate stacks have been well chronicled. [4,5] In examining the threshold voltage equation,The term V FB is composed of the several charge terms and the metal-semiconductor work function difference, MS = Metal -semi . In reducing V Tp , researchers have searched for a metal with high Metal to maximize MS and offset the other terms in V Tp .[6] However, an alternate approach is to minimize semi . This can be done by incorporating Ge into the channel which is known to move the valence band toward the vacuum level. [7] A comparison of the 1µm I D -V G curves for >10% SiGe channel and control Si in Fig. 1 indicates there is a shift of ~300mV and that the drive current for the SiGe device is significantly higher than the Si. This phenomenon is seen for several metals that are within ¼E g of the valence band edge (Fig. 2). The lower threshold voltage is attributed to two mechanisms: the change in band gap due to Ge in the SiGe [7] with a minor contribution from compressive strain of epitaxial SiGe directly on Si. [8] This is shown pictorially in Fig. 3 where a metal with work function of ~4.9 eV [9] is lined up with the valence band of the epitaxial SiGe. In Fig. 4, the gate leakage and C-V of ALD HfSiO directly on SiGe display excellent properties with EOT= 1.25nm and J G = 9.2A/cm 2 .Since a SiGe channel can provide the right PMOS threshold voltage, it is of interest to combine it with known NMOS solutions and demonstrate high performance. The integration scheme for combining these elements on the same wafer is outlined in Fig. 5. [10] Afer the NMOS gate stack is deposited and masked off, the PMOS Si is slightly recessed and selective epitaxy of >10% SiGe is grown. The PMOS gate stack is then deposited and masked. After removal of the PMOS gate from the top of the NMOS mask, the hard masks are then removed and the poly is deposited. After this point standard planar CMOS processing is followed with 1070 o C activation anneal.Cross section TEMs of the PMOS devices at ~80nm and the gate stack HRTEM are seen in Fig. 6. The detai...

We present a theoretical analysis of the noise behavior of a heterojunction bipolar transistor (HBT) used as a three terminal (3T) photodetector. The use of a HBT in the photodetector mode can greatly simplify the fabrication of HBT-based optical receivers in the monolithic form. The present model takes into account the effect of the received light on the intrinsic parameters and various noise components of the HBT when used as a detector. The model enables one to determine the signal-to-noise ratio at the output and also the noise equivalent power of the HBT. The model has been applied for characterization of an InP/ InGaAs HBT used as a 3T photodetector in the 1.55-m wavelength region.

Pressure waves from Ho:YAG lithotripsy are less than with other modalities, yet some retropulsion occurs. The duration of the laser pulse can influence shockwave generation and object migration. Longer pulse width results in less object movement after one shock and more energy delivery during repetitive shocks. Clinically, this regimen may reduce the need for fiber readjustment and lead to more efficient stone fragmentation.

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