Simplified 0.35-μm flash EEPROM process using high-temperatureoxide (HTO) deposited by LPCVD as interpoly dielectrics and peripheraltransistors gate oxide

A simplified flash EEPROM process was developed using high-temperature LPCVD oxide both as flash cells interpoly dielectrics and as peripheral transistors gate oxide (decoding logic). An O₂ anneal at 850°C lowers charge trapping and interface trap density induced by Fowler-Nordheim injection. However, electron trapping remains slightly higher than with dry thermal oxide. Similar memory charge loss and write-erase endurance are obtained as for ONO-insulated cells. HTO thus proves to have the required quality and reliability to be used in flash EEPROMs     

Erbium-doped silicon and porous silicon for optoelectronics

Silicon forms the backbone of the microelectronics industry, and possibly, of the optoelectronics industry, hitherto dominated by III/V materials. One of the remaining goals is to build an optical source in silicon. Erbium exhibits luminescent 1.54 μm intra-4f transitions in both silicon and porous silicon. This paper reviews the work which has been carried out in this field and discusses some possible additional applications of erbium-doped silicon in optoelectronics, such as a novel on-chip temperature sensor.     

Cutoff frequencies and noise power law model of spectral density:adaptation of the multivariance method for irregularly spaced timingdata using the lowest-mode estimator approach

The concept of structure functions, which is an extension of the variance approach, is useful to determine the variance (the structure function) which is optimized for a type of noise and for an order of drift. The multivariance method was developed to use different variances over the same signal. It is then possible to select a set of variances in which each variance is optimized to the determination of one parameter (of one noise level, drift, or cutoff frequency). Recently, we adapted this method to irregularly spaced timing data. In this connection, we replaced the structure functions by another method of spectral density estimation: the lowest-mode estimator, introduced by J.E. Deeter and P.E. Boynton (1982, 1984) for the analysis of pulsar timing data. Different lowest-mode estimators can be constructed according to two priorities: the order of drifts that must be removed and the type of noise for which the sensitivity must be maximum. Thus, a multivariance system is developed using a set of different estimators. The details of this method are described, and the results for different signals are discussed in this paper     

Oxygen absorption kinetics in YBa2Cu3Ox

The oxygen absorption process in highly aligned YBa₂Cu₃O_x samples in air and in pure oxygen was studied at several constant temperatures. The process can be expressed in two steps, a chemical reaction controlled step and a diffusion controlled step. The kinetic equations are as follows respectively:

(1 − a_t) ln(1 − a_t) + a_t = k_Dt

The activation energy was calculated using an Arrhenius equation. The technical conditions of heat treatment for oxygen absorption of highly aligned YBa₂Cu₃O_x crystals are discussed.     

A stochastic deconvolution method to reconstruct insulin secretionrate after a glucose stimulus

Insulin secretion rate (ISR) is not directly measurable in man but it can be reconstructed from C-peptide (CP) concentration measurements by solving an input estimation problem by deconvolution. The major difficulties posed by the estimation of ISR after a glucose stimulus, e.g., during an intravenous glucose tolerance test (IVGTT), are the ill-conditioning of the problem, the nonstationary pattern of the secretion rate, and the nonuniform/infrequent sampling schedule. In this work, a nonparametric method based on the classic Phillips-Tikhonov regularization approach is presented. The problem of nonuniform/infrequent sampling is addressed by a novel formulation of the regularization method which allows the estimation of quasi time-continuous input profiles. The input estimation problem is stated into a Bayesian context, where the a priori known nonstationary characteristics of ISR after the glucose stimulus are described by a stochastic model. Deconvolution is tackled by linear minimum variance estimation, thus allowing the derivation of new statistically based regularization criteria. Finally, a Monte-Carlo strategy is implemented to assess the uncertainty of the estimated ISR arising from CP measurement error and impulse response parameters uncertainty