200-V lateral superjunction LIGBT on partial SOI

This letter presents a novel lateral superjunction lateral insulated-gate bipolar transistor (LIGBT) in partial silicon-on-insulator (SOI) technology in 0.18-μm partial-SOI (PSOI) high-voltage (HV) process. For an n-type superjunction LIGBT, the p-layer in the superjunction drift region not only helps in achieving uniform electric field distribution but also contributes to the on-state current. The superjunction LIGBT successfully achieves a breakdown voltage (BV) of 210 V with an R dson of 765 mΩ ̇ mm 2. It exhibits half the value of specific on-state resistance R dson and three times higher saturation current (I dsat) for the same BV, compared to a comparable lateral superjunction laterally diffused metal-oxide-semiconductor fabricated in the same technology. It also performs well in higher temperature dc operation with 38.8% increase in R dson at 175°C, compared to the room temperature without any degradation in latch-up performance. To realize this device, it only requires one additional mask layer into X-FAB 0.18-μm PSOI HV process. © 2012 IEEE.

The lateral superjunction PSOI LIGBT and LDMOSFET

This paper presents a comparison between the superjunction LIGBT and the LDMOSFET in partial silicon-on-insulator (PSOI) technology in 0.18μm PSOI HV process. The superjunction drift region helps in achieving uniform electric field distribution in both structures but also contributes to the on-state current in the LIGBT. The superjunction LIGBT successfully achieves breakdown voltage (BV) of 210V with Rdson of 765mΩ.mm2. It exhibits reduced specific on-state resistance Rdson and higher saturation current (Idsat) for the same BV compared to a compatible lateral superjunction LDMOS in the same technology. © 2012 IEEE.

200 v superjunction N-type lateral insulated-gate bipolar transistor with improved latch-up characteristics

This paper evaluates the technique used to improve the latching characteristics of the 200 V n-type superjunction (SJ) lateral insulated-gate bipolar transistor (LIGBT) on a partial silicon-on-insulator. SJ IGBT devices are more prone to latch-up than standard IGBTs due to the presence of a strong pnp transistor with the p layer serving as an effective collector of holes. The initial SJ LIGBT design latches at about 23 V with a gate voltage of 5 V with a forward voltage drop (VON) of 2 V at 300 Acm2. The latch-up current density is 1100 Acm2. The latest SJ LIGBT design shows an increase in latch-up voltage close to 100 V without a significant penalty in VON. The latest design shows a latch-up current density of 1195 A cm2. The enhanced robustness against static latch-up leads to a better forward bias safe operating area. © 1963-2012 IEEE.

200V superjunction lateral IGBT fabricated on partial SOI

A 200V lateral insulated gate bipolar transistor (LIGBT) was successfully developed using lateral superjunction (SJ) in 0.18μm partial silicon on insulator (SOI) HV process. The results presented are based on extensive experimental measurements and numerical simulations. For an n-type lateral SJ LIGBT, the p layer in the SJ drift region helps in achieving uniform electric field distribution. Furthermore, the p-pillar contributes to the on-state current. Furthermore, the p-pillar contributes to sweep out holes during the turn-off process, thus leading to faster removal of plasma. To realize this device, one additional mask layer is required in the X-FAB 0.18μm partial SOI HV process. © 2013 IEEE.

Analysis on the off-state design and characterization of LIGBTs in partial SOI technology

Classical high voltage devices fabricated on SOI substrates suffer from a backside coupling effect which could result in premature breakdown. This phenomenon becomes more prominent if the structure is an IGBT which features a p-type injector. To suppress the premature breakdown due to crowding of electro-potential lines within a confined SOI/buried oxide structure, the partial SOI (PSOI) technique is being introduced. This paper analyzes the off-state behavior of an n-type Superjunction (SJ) LIGBT fabricated on PSOI substrate. During the initial development stage the SJ LIGBT was found to have very high leakage. This was attributed to the back and side coupling effects. This paper discusses these effects and shows how this problem could be successfully addressed with minimal modifications of device layout. The off-state performance of the SJ LIGBT at different temperatures is assessed and a comparison to an equivalent LDMOSFET is given. © 2014 Elsevier Ltd. All rights reserved.

A Cancer Recognition Method Based on DNA Microarray

The accurate cancer classification is of great importance in clinical treatment. Recently, the DNA microarray technology provides a promising approach to the diagnosis and prognosis of cancer types. However, it has no perfect method for the multiclass classification problem. The difficulty lies in the fact that the data are of high dimensionality with small sample size. This paper proposed an automatic classification method of multiclass cancers based on Biomimetic pattern recognition (BPR). To the public GCM data set, the average correct classification rate reaches 80% under the condition that the correct rejection rate is 81%.

Study on Closed-set Speaker Identification Based on Biomimetic Pattern Recognition

In this paper, a new classifier of speaker identification has been proposed, which is based on Biomimetic pattern recognition (BPR). Distinguished from traditional speaker recognition methods, such as DWT, HMM, GMM, SVM and so on, the proposed classifier is constructed by some finite sub-space which is reasonable covering of the points in high dimensional space according to distributing characteristic of speech feature points. It has been used in the system of speaker identification. Experiment results show that better effect could be obtained especially with lesser samples. Furthermore, the proposed classifier employs a much simpler modeling structure as compared to the GMM. In addition, the basic idea "cognition" of Biomimetic pattern recognition (BPR) results in no requirement of retraining the old system for enrolling new speakers.

An Automatic Classification Method of Metabolic Cycle Based on Biomimetic Pattern Recognition

Correct classification of different metabolic cycle stages to identification cell cycle is significant in both human development and clinical diagnostics. However, it has no perfect method has been reached in classification of metabolic cycle yet. This paper exploringly puts forward an automatic classification method of metabolic cycle based on Biomimetic pattern recognition (BPR). As to the three phases of yeast metabolic cycle, the correct classification rate reaches 90%, 100% and 100% respectively.

Biomimetic pattern recognition theory and its applications

Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.

Double synaptic weight neuron theory and its application

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

Origin of deep level defect related photoluminescence in annealed InP

Deep level defects in annealed InP have been studied by using photoluminescence spectroscopy (PL), thermally stimulated current (TSC), deep level transient spectroscopy (DLTS), and positron annihilation lifetime (PAL). A noticeable broad PL peak centered at 1.3 eV has been observed in the InP sample annealed in iron phosphide ambient. Both the 1.3 eV PL emission and a defect at E-C-0.18 eV correlate with a divacancy detected in the annealed InP sample. The results make a divacancy defect and related property identified in the annealed InP. (c) 2006 American Institute of Physics.

Discussion on the basic mathematical models of neurons - Double synaptic weight neuron in general-purpose neurocomputer, theory and application

Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.

A speaker-independent continuous speech recognition system using biomimetic pattern recognition

In speaker-independent speech recognition, the disadvantage of the most diffused technology (HMMs, or Hidden Markov models) is not only the need of many more training samples, but also long train time requirement. This paper describes the use of Biomimetic pattern recognition (BPR) in recognizing some mandarin continuous speech in a speaker-independent manner. A speech database was developed for the course of study. The vocabulary of the database consists of 15 Chinese dish's names, the length of each name is 4 Chinese words. Neural networks (NNs) based on Multi-weight neuron (MWN) model are used to train and recognize the speech sounds. The number of MWN was investigated to achieve the optimal performance of the NNs-based BPR. This system, which is based on BPR and can carry out real time recognition reaches a recognition rate of 98.14% for the first option and 99.81% for the first two options to the persons from different provinces of China speaking common Chinese speech. Experiments were also carried on to evaluate Continuous density hidden Markov models (CDHMM), Dynamic time warping (DTW) and BPR for speech recognition. The Experiment results show that BPR outperforms CDHMM and DTW especially in the cases of samples of a finite size.