1. A thin film transistor device comprised of a substrate, a gate electrode, a gate dielectric layer, a source electrode and a drain electrode, and in contact with the sourcedrain electrodes and the gate dielectric layer, a semiconductor layer comprised of a polythiophene represented by Formula (III)
wherein A is a long side chain containing at least about 5 carbon atoms; B is hydrogen or a short side chain containing from about 1 to about 4 carbon atoms; and D is a divalent segment; a and c represent the number of A-substituted thienylenes, wherein a is at least 2; b is the number of B-substituted thienylene units and is from 1 to about 6; d is 1; c and m are independently 1,2, or 3; and n is the degree of polymerization or the number of the monomer segments in the polythiophene, and wherein the polythiophene has an Mn between about 4,000 and about 50,000.
2. A thin film transistor device in accordance with claim 1 wherein D is a divalent linkage selected from the group consisting of a saturated moiety of alkylene, \u2014O\u2014R\u2014O\u2014, \u2014S\u2014R\u2014S\u2014, \u2014NH\u2014R\u2014NH\u2014, where R is alkylene or arylene, an unsaturated moiety of an arylene, and heteroaromatics.
3. A thin film transistor device in accordance with claim 1 wherein A is alkyl containing from 6 to about 25 carbon atoms; B is hydrogen or alkyl containing from 1 to about 3 carbon atoms; D is arylene or dioxyarene, each containing from about 6 to about 40 carbon atoms, or alkylene or dioxyalkane, each containing from about 1 to about 20 carbon atoms.
4. A thin film transistor device in accordance with claim 1 wherein A is alkyl containing from about 8 to about 12 carbon atoms, and B is a hydrogen atom.
5. A thin film transistor device in accordance with claim 1 wherein A is alkyl containing from 5 to about 15 carbon atoms; B is a hydrogen atom; D is arylene; a, b, c, and m are independently selected from the numbers 1,2, and 3; and d =1.
6. A thin film transistor device in accordance with claim 1 wherein A is alkyl containing from about 8 to about 12 carbon atoms; B is a hydrogen atom; D is arylene; a=c=m=1; b=2; and d =1.
7. A thin film transistor device in accordance with claim 1 wherein n is from about 5 to about 5,000.
8. A thin film transistor device in accordance with claim 1 wherein the weight average molecular weight (Mw) is from about 4,000 to about 500,000 as measured by gel permeation chromatography using polystyrene standards.
9. A thin film transistor device in accordance with claim 1 wherein the number average molecular weight (Mn) of (III) is from about 10,000 to about 30,000 and the weight average molecular weight (Mw) is from about 15,000 to about 100,000.
10. A thin film transistor device in accordance with claim 1 wherein A is hexyl, heptyl, octyl, nonyl, decyl, undecyl, dodecyl, tridecyl, tetradecyl, or pentyldecyl.
11. A thin film transistor device in accordance with claim 1 wherein D is an arylene selected from the group consisting of phenylene, tolylene, xylylene, biphenylene, substituted biphenylene, fluorenylene, phenanthrenylene, dihydrophenanthrenylene, and dibenzofuranediyl, dibenzothiophenediyl, carbazole-diyl.
12. A thin film transistor device in accordance with claim 1 wherein D is saturated linkage selected from the group consisting of alkylene, dioxyalkane, dioxyarene, and oligoethylene oxide.
13. A thin film transistor device in accordance with claim 1 wherein said polythiophene (III) is selected from (1) through (17) wherein n represents the number of repeating segments
14. A thin film transistor device in accordance with claim 1 wherein polythiophene (III) is alternatively wherein n represents the number of segments
15. A thin film transistor device in accordance with claim 1 wherein polythiophene (III) is alternatively wherein n represents the number of segments
16. A thin film transistor device in accordance with claim 1 wherein said polythiophene is alternatively
17. A thin film transistor device in accordance with claim 1 wherein said substrate is a plastic sheet of a polyester, a polycarbonate, or a polyimide; said gate, source, and drain electrodes are each independently comprised of gold, nickel, aluminum, platinum, or indium titanium oxide; and said gate dielectric layer is comprised of silicon nitride, silicon oxide, insulating polymers of polyester, polycarbonates, polyacrylate, poly(methacrylate), poly(vinyl phenol), polystyrene, polyimide, or an epoxy resin.
18. A thin film transistor device in accordance with claim 1 wherein said substrate is glass or a plastic sheet; said gate, source and drain electrodes are each independently comprised of gold or a metal dispersion in a binder; said gate dielectric layer is comprised of an organic polymer of polyester, polycarbonate, polyacrylate, poly(methacrylate), poly(vinyl phenol), polystyrene, polyimide, or an epoxy resin, or an inorganic-organic composite of nanosized metal oxide particles dispersed in a polymer of a polyester, a polyimide, or an epoxy resin.
19. A thin film transistor device in accordance with claim 1 wherein the thickness of the substrate is from about 10 micrometers to about 10 millimeters; the thickness of the gate dielectric layer is from about 10 nanometers to about 1 micrometer; the thickness of the polythiophene semiconductor layer is from about 10 nanometers to about 1 micrometer; the thickness of the gate electrode layer is from about 10 nanometers to about 10 micrometers; and the thickness of the source or drain electrode is from about 40 nanometers to about 1 micrometer.
20. A thin film transistor device in accordance with claim 1 wherein A is alkoxyalkyl, a polyether chain, perhaloalkyl, alkyl, or alkoxy.
21. A thin film transistor device in accordance with claim 1 wherein A is methoxybutyl, methoxyhexyl, methoxyheptyl, polyethylene oxide, perfluoroalkyl, trialkylsiloxyalkyl, and B is methoxy, ethoxy, propoxy, or butoxy.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A machine learning method comprising:
providing one or more training data samples having one or more known classes;
training two or more learning machines to identify said one or more known classes using said one or more training data samples;
selecting the trained learning machine that optimizes a performance function dependent on one or more variables between said one or more known classes selected from the group consisting of maximizing divergence between the classes of data, n-fold cross validation, number of support vectors chosen, Vapnik-Chervonenkis (VC) dimension, ratio of all support vectors to unbounded support vectors, relative magnitude of the parameters concerned, favoring a large sigma for a Gaussian kernel, and small upper bound for Lagrange multipliers of each point; and
outputting the selected trained learning machine into a computer memory.
2. The method of claim 1, wherein the selected trained learning machine is output on an output device.
3. The method of claim 1, further comprising:
conducting a query of data contained in a database to identify data corresponding to said one or more known classes using the selected, trained learning machine; and
outputting data identified by the query as corresponding to said one or more known classes on an output device.
4. The method of claim 3, wherein the data contained in the database have unknown classes, and the data are identified as corresponding to said one or more known classes by comparing patterns identified in said one or more unknown data samples with patterns identified in said one or more training data samples.
5. The method of claim 1, wherein the one or more training data samples having one or more known classes are subjected to a feature reduction step prior to training to form training data samples each having differently reduced dimensionality, and wherein the two or more learning machines are trained using said training data samples having differently reduced dimensionality.
6. The method of claim 1, wherein the learning machine comprises a sequential minimal optimization (SMO) algorithm, and each training data sample is assigned a weight within the SMO.
7. The method of claim 6, wherein the weight is assigned to the training data sample by a user.
8. The method of claim 6, wherein the weight is automatically assigned based on detection of quality measures within the training data sample.
9. The method of claim 1, wherein the learning machine is selected from the group consisting of a Support Vector Machine (SVM), a decision tree, a hidden Markov model, a Bayesian network, a Gram Schmidt algorithm, a neural network, and a genetic algorithm.
10. The method of claim 1, wherein the one or more training data samples having one or more known classes contain supplemental data selected from the group consisting of normal data samples, negative data samples, randomly-generated data samples, theoretical data samples, and combinations thereof.
11. The method of claim 1, wherein selecting the trained learning machine comprises the steps of providing a computer-readable program code means for comparing performance measures of the trained learning machines and predicting querying success.
12. The method of claim 1, wherein the one or more training data samples having one or more known classes are subjected to a feature reduction step prior to training to form training data samples each having differently reduced dimensionality, and wherein the two or more learning machines are trained using said training data samples having differently reduced dimensionality.
13. A machine learning method comprising:
providing one or more data patterns;
providing one or more data samples;
training two or more learning machines to identify which of the one or more data samples correspond to the one or more data patterns;
selecting the trained learning machine that identifies which of the one or more data samples correspond to the one or more data patterns by optimizing a performance function dependent on one or more variables selected from the group consisting of maximizing divergence between the classes of data, n-fold cross validation, number of support vectors chosen, Vapnik-Chervonenkis (VC) dimension, ratio of all support vectors to unbounded support vectors, relative magnitude of the parameters concerned, favoring a large sigma for a Gaussian kernel, and small upper bound for Lagrange multipliers of each point; and
outputting the selected trained learning machine into a computer memory.
14. The method of claim 13, wherein the selected trained learning machine is output on an output device.
15. The method of claim 13, further comprising:
conducting a query of data contained in a database to identify data corresponding to said one or more data patterns using the selected, trained learning machine; and
outputting data identified by the query as corresponding to said one or more data patterns on an output device.
16. The method of claim 15, wherein the data are identified as corresponding to said one or more data patterns by comparing said one or more unknown data samples with patterns identified in said one or more data samples.
17. The method of claim 13, wherein the one or more data samples are subjected to a feature reduction step prior to training to form training data samples having differently reduced dimensionality, and wherein the two or more learning machines are trained using said training data samples having differently reduced dimensionality.
18. The method of claim 13, wherein the learning machine is selected from the group consisting of a Support Vector Machine (SVM), a decision tree, a hidden Markov model, a Bayesian network, a Gram Schmidt algorithm, a neural network, a genetic algorithm, and a sequential minimal optimization (SMO) algorithm.
19. A computer program product comprising a computer usable medium having control logic stored therein for causing a computer to produce a trained learning machine, the control logic comprising:
first computer readable program code means for providing one or more training data samples having one or more known classes;
second computer readable program code means for training two or more learning machines using said one or more training data samples;
third computer readable program code means for selecting the trained learning machine that maximizes divergence between the classes of data; and
fourth computer readable program code means for outputting the selected trained learning machine on an output device.
20. The computer program product of claim 19, wherein the computer usable medium is provided in a computer system.