Author: claimsbot

1. A thin film transistor, comprising:
a gate electrode;
a source electrode;
a drain electrode facing the source electrode;
an oxide semiconductor layer disposed between the gate electrode and the source electrode or between the gate electrode and the drain electrode; and
a gate insulating layer disposed between the gate electrode and the source electrode or between the gate electrode and the drain electrode,
wherein when a signal applied to the gate electrode is a turn-off signal, a voltage applied to the gate electrode has a negative value.
2. The thin film transistor of claim 1, wherein the thin film transistor is included in a gate driver of a display device.
3. The thin film transistor of claim 2, wherein the thin film transistor is directly connected with an output terminal of the gate driver, and the output terminal is connected with a gate line and outputs a gate signal.
4. The thin film transistor of claim 3, wherein the negative value is between about \u221220 V and about \u22123 V.
5. The thin film transistor of claim 4, wherein the oxide semiconductor layer includes indium gallium zinc oxide.
6. The thin film transistor of claim 1, wherein the oxide semiconductor layer is disposed on the gate electrode.
7. The thin film transistor of claim 1, wherein the gate electrode is disposed on the oxide semiconductor layer.
8. The thin film transistor of claim 1, further comprising an etching protection layer disposed on the oxide semiconductor layer and covering a channel area of the oxide semiconductor layer.
9. The thin film transistor of claim 1, wherein the gate electrode is wider than a channel area of the oxide semiconductor layer.
10. The thin film transistor of claim 9, wherein a thickness of the gate insulating layer is uniform in a portion of the gate insulating layer corresponding to the channel area.
11. The thin film transistor of claim 1, further comprising a floating metal layer disposed between the source electrode and the drain electrode.
12. The thin film transistor of claim 11, wherein the floating metal layer is electrically floated.
13. The thin film transistor of claim 12, wherein the floating metal layer overlaps a channel area of the oxide semiconductor layer.
14. The thin film transistor of claim 13, wherein the floating metal layer is disposed on a same layer as the source electrode and the drain electrode.
15. The thin film transistor of claim 14, wherein the floating metal layer has an island shape.
16. The thin film transistor of claim 15, further comprising an etching protection layer disposed between the floating metal layer and the oxide semiconductor layer.
17. The thin film transistor of claim 1, wherein a thickness of the gate insulating layer is uniform in a portion of the gate insulating layer corresponding to a channel area of the oxide semiconductor layer.
18. The thin film transistor of claim 17, wherein the negative value is between about \u221220V and about \u22123V.
19. The thin film transistor of claim 17, wherein the gate electrode is wider than the channel area of the oxide semiconductor layer.
20. The thin film transistor of claim 1, wherein when the gate electrode is applied with a negative voltage to turn off the thin film transistor, there is no conduction between the source electrode and the drain electrode.

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 method of identifying a configuration of an object that satisfies a predetermined optimisation criterion under specified conditions, the method comprising the steps of:
(i) specifying a plurality of different object configurations;
(ii) for each specified object configuration, using a first simulation procedure to simulate the specified conditions so as to generate data that can be used to evaluate the object configuration against the optimisation criterion, wherein the first simulation procedure comprises iteratively calculating values of variables characterising the object configuration;
(iii) identifying a functional relationship between the data generated for each specified object configuration;
(iv) using the functional relationship and the optimisation criterion to select a data point, and identifying an object configuration corresponding thereto; and
(v) using a second simulation procedure, simulating the specified conditions in respect of the identified object configuration, wherein the second simulation procedure comprises iteratively calculating values of variables characterising the object configuration until the values satisfy a convergence criterion,
wherein, for at least one of the specified object configurations, the first simulation procedure is completed before the values calculated therein satisfy the convergence criterion used in the second simulation procedure.
2. A method according to claim 1, wherein, for at least one of the specified object configurations, the values calculated on completion of the first simulation procedure are less than 99% correlated with values calculated on completion of the second simulation procedure, using the same object configuration.
3. A method according to claim 1, wherein the method comprises completing the first simulation procedure when the values calculated therein satisfy a convergence criterion which is different to the convergence criterion used in the second simulation procedure.
4. A method according to claim 3, wherein the convergence criterion used in the first simulation procedure is applied by calculating a correlation coefficient indicative of correlation in respect of successive iterations of values of variables and determining whether the calculated correlation coefficient is substantially equal to a specified correlation coefficient.
5. A method according to claim 4, including calculating an average of a succession of two or more correlation coefficients and determining whether the calculated average is substantially equal to a specified value.
6. A method according to claim 3, wherein convergence criterion used in the first simulation procedure is applied separately to values calculated for each specified object configuration.
7. A method according to claim 3, wherein convergence criterion used in the first simulation procedure is applied collectively to values calculated for each specified object configuration.
8. A method according to claim 7, wherein the convergence criterion used in the first simulation procedure is applied by identifying a different functional relationship between data generated for each specified object configuration in different iterations, and applying the convergence criterion to the different functional relationships.
9. A method according to claim 8, in which the functional relationship comprises a plurality of gradients and the method includes evaluating at least one of the plurality of gradients in the different functional relationships.
10. A method according to claim 1, wherein said second simulation procedure comprises evaluating data generated in the second simulation procedure against the optimisation criterion, and selecting the identified object configuration corresponding to said data if the optimisation criterion is met.
11. A method according to claim 1, wherein said second simulation procedure comprises evaluating data generated in the second simulation procedure against the optimisation criterion, and if the data does not meet the optimisation criterion, the method includes adding the identified object configuration to the plurality of specified object configurations and repeating steps (iii) to (v) until the identified object configuration satisfies the optimisation criterion.
12. A method according to claim 1, wherein the object comprises an aircraft structure.
13. A method according to claim 12, wherein the object comprises a flap track fairing associated with an aircraft wing.
14. A method according to claim 12, in which the variables include lift and drag associated with the object.
15. A data processing system arranged to perform the method according to claim 1.
16. Computer software arranged to perform the method according to claim 1.
17. A data carrier on which is supported computer software arranged to perform the method according to claim 1.

What is claimed is:

1. A diagnostic tool for use in a process control system having a multiplicity of function blocks, the diagnostic tool comprising:
a data collection unit configured to communicate with each of the multiplicity of function blocks to receive, on a regular basis during operation of the process control system, data pertaining to a function block operating parameter for each of the multiplicity of function blocks;
a data analyzer that determines a value for the function block operating parameter for each of a number of times during operation of the process control system based on the received function block operating parameter data;
a detector that detects a problem within the process control system based on the determined values of the function block operating parameter; and
an output generator that creates a report indicating the detected problem.
2. The diagnostic tool of claim 1, wherein the function block operating parameter is a variability parameter and wherein the data analyzer determines a variability value associated with one of the function blocks at each of the number of times based on the collected function block operating parameter data.
3. The diagnostic tool of claim 2, wherein the detector compares the variability value with a variability limit to detect the problem.
4. The diagnostic tool of claim 2, wherein the variability value is indicative of an integral squared error between a first function block parameter and a second function block parameter.
5. The diagnostic tool of claim 4, wherein the first function block parameter is a statistical value of a process control parameter and the second function block parameter is an instantaneous value of the process control parameter.
6. The diagnostic tool of claim 2, wherein the one of the function blocks is associated with an input device that measures a process parameter and wherein the variability value for the one of the function blocks comprises an indication of an error between a statistical value of the measured process parameter and an instantaneous value of the measured process parameter.
7. The diagnostic tool of claim 2, wherein the one of the function blocks uses a process parameter signal and a set point to produce a control signal and wherein the variability value for the one of the function blocks comprises an indication of an error between the process parameter and the set point.
8. The diagnostic tool of claim 2, wherein the function block operating parameter data received from each of the function blocks includes a first variability indication indicative of a mean absolute error of a function block parameter and a second variability indication indicative of a moving range average of the function block parameter.
9. The diagnostic tool of claim 2, wherein the function block operating parameter data received from each of the function blocks includes a first variability indication indicative of an actual total standard deviation of a function block parameter and a second variability indication indicative of a capability standard deviation associated with the function block parameter.
10. The diagnostic tool of claim 9, wherein the data analyzer combines the first variability indication with the second variability indication to produce the variability value.
11. The diagnostic tool of claim 10, wherein the data analyzer adds a sensitivity value to each of the first and second variability indications to produce the variability value.
12. The diagnostic tool of claim 2, wherein the function block operating parameter data comprises the variability value.
13. The diagnostic tool of claim 1, wherein the function block operating parameter is a mode parameter and wherein the data collection unit receives a mode indication for each of the function blocks.
14. The diagnostic tool of claim 13, wherein the data analyzer determines a mode value for one of the function blocks specifying if the one of the function blocks is in a normal mode in which the one of the function blocks was designed to operate during normal operation.
15. The diagnostic tool of claim 13, wherein the data analyzer determines a mode value for one of the function blocks as a percentage of a specific period of time that the mode of the one of the function blocks was a non-normal mode associated with the one of the function blocks.
16. The diagnostic tool of claim 15, wherein the detector compares the mode value for the one of the function blocks to a mode limit to detect the problem.
17. The diagnostic tool of claim 1, wherein the function block operating parameter is a status parameter and wherein the data collection unit receives status indications for each of the multiplicity of function blocks.
18. The diagnostic tool of claim 17, wherein the data analyzer determines a status value from the status indications indicating if a status associated with one of the function blocks is a normal status associated with the normal operation of the one of the function blocks.
19. The diagnostic tool of claim 17, wherein the data analyzer determines a status value for one of the function blocks as a percentage of a specific period of time that the status of the one of the function blocks was a non-normal status associated with the one of the function blocks.
20. The diagnostic tool of claim 19, wherein the detector compares the status value for the one of the function blocks to a status limit to detect the problem.
21. The diagnostic tool of claim 1, wherein the function block operating parameter is limit parameter and the data collection unit collects limit indications associated with a function block variable.
22. The diagnostic tool of claim 21, wherein the data analyzer determines a limit value from the limit indications indicating if the function block variable is at one or more limits.
23. The diagnostic tool of claim 21, wherein the data analyzer determines a limit value as a percentage of a specific period of time that the function block variable was at one or more limits.
24. The diagnostic tool of claim 1, wherein the data collection unit further collects an application state parameter from one of the multiplicity of function blocks and wherein the detector ignores the function block operating parameter data associated with the one of the multiplicity of function blocks to detect the problem when the function block operating parameter data is associated with a time in which the application state parameter was in a first state and the detector uses the function block operating parameter data associated with the one of the multiplicity of function blocks to detect the problem when the function block operating parameter data is associated with a time in which the application state parameter was in a second state.
25. The diagnostic tool of claim 1, wherein the detector detects the problem based on a current value of the function block operating parameter for one of the multiplicity of function blocks.
26. The diagnostic tool of claim 1, wherein the detector detects the problem based on a plurality of past values for the function block operating parameter for one of the function blocks.
27. The diagnostic tool of claim 1, wherein the detector compares the value of the function block operating parameter to a preset limit and further including a communicator that enables the preset limit to be specified by a user.
28. The diagnostic tool of claim 1, wherein the detector detects the problem based on a plurality of values of the function block operating parameter over a particular time and further including a communicator that enables the particular time to be specified by a user.
29. The diagnostic tool of claim 1, further including a recommendation unit that recommends use of a further tool to correct the detected problem.
30. The diagnostic tool of claim 29, wherein the recommendation unit recommends the use of a tuner.
31. The diagnostic tool of claim 29, wherein the recommendation unit recommends the use of a calibrator.
32. The diagnostic tool of claim 29, wherein the recommendation unit implements the recommended further tool.
33. The diagnostic tool of claim 29, wherein the further tool requires collection of process parameter data of a process parameter and further including a further data collection unit that automatically collects samples of the process parameter as the process parameter data using a trend object during operation of the process control system.
34. The diagnostic tool of claim 33, wherein the trend object is a virtual trend object created by the further data collection unit.
35. The diagnostic tool of claim 33, wherein the trend object is created by one of the function blocks.
36. A diagnostic tool for use in a process control system that includes a processor and that uses a multiplicity of function blocks to control a process, the diagnostic tool comprising:
a computer readable memory; and
a routine stored on the computer readable memory and adapted to be implemented on the processor, wherein the routine;
collects data pertaining to a function block operating parameter for each of the multiplicity of function blocks on a regular basis during operation of the process;
determines a value for the function block operating parameter for each of a number of times during the operation of the process control system based on the collected function block operating parameter data;
detects a problem within the process control system based on the determined values of the function block operating parameter; and
produces a report that lists the detected problem.
37. The diagnostic tool of claim 36, wherein the function block operating parameter is a variability parameter and wherein the routine collects variability indications for each of the multiplicity of function blocks.
38. The diagnostic tool of claim 37, wherein the routine determines a variability value for one of the function blocks from the variability indications collected from the one of the function blocks and compares the variability value to a variability limit to detect the problem.
39. The diagnostic tool of claim 36, wherein the function block operating parameter is a mode parameter and wherein the routine collects mode indications for each of the multiplicity of function blocks.
40. The diagnostic tool of claim 39, wherein the routine determines a mode value for one of the function blocks as a percentage of a specific period of time that the one of the function blocks was in a non-normal mode associated with the one of the function blocks.
41. The diagnostic tool of claim 36, wherein the function block operating parameter is a status parameter and wherein the routine collects status indications for each of the multiplicity of function blocks.
42. The diagnostic tool of claim 41, wherein the routine determines a status value for one of the function blocks as a percentage of a specific period of time that a status associated with the one of the function blocks was a non-normal status.
43. The diagnostic tool of claim 36, wherein the function block operating parameter is limit parameter and the routine collects limit indications associated with a function block variable.
44. The diagnostic tool of claim 43, wherein the routine determines a limit value from the limit indications as a percentage of a specific period of time that the function block variable was at one or more limits.
45. The diagnostic tool of claim 36, wherein the routine further collects an application state parameter from one of the multiplicity of function blocks and ignores the function block operating parameter data associated with the one of the multiplicity of function blocks to detect the problem when the function block operating parameter data is associated with a time in which the application state parameter was in a first state and uses the function block operating parameter data associated with the one of the multiplicity of function blocks to detect the problem when the function block operating parameter data is associated with a time in which the application state parameter was in a second state.
46. The diagnostic tool of claim 36, wherein the routine detects the problem based on a multiplicity of historical values of the function block operating parameter for one of the multiplicity of function blocks.
47. The diagnostic tool of claim 36, wherein the routine recommends the use of a further tool to correct the detected problem.
48. The diagnostic tool of claim 47, wherein the routine recommends one of a tuner routine, a calibration routine and a data analysis routine as the further tool.
49. The diagnostic tool of claim 47, wherein the routine implements the recommended further tool.
50. The diagnostic tool of claim 47, wherein the routine recommends the use of a further tool that requires the collection of process parameter data for a process parameter and automatically collects samples of the process parameter as the process parameter data during operation of the process control system.
51. A method of diagnosing problems in a process control system that uses a multiplicity of function blocks to control the operation of a process, the method comprising the steps of:
collecting data pertaining to a function block operating parameter from each of the multiplicity of function blocks during operation of the process control system;
determining a value for the function block operating parameter for each of a number of times during operation of the process control system based on the received function block operating parameter data;
detecting a problem within the process control system based on the determined function block operating parameter values; and
reporting the detected problem to a user.
52. The method of claim 51, wherein the function block operating parameter is a variability parameter and the step of collecting data includes the step of collecting variability indications for each of the multiplicity of function blocks.
53. The method of claim 52, further including the step of calculating first and second variability indications in each of the function blocks as the function block operating parameter data, wherein the step of collecting includes the step of sending the first and second variability indications from each of the function blocks to a data analyzer and further including the step of calculating a value of the variability parameter for each of the function blocks in the data analyzer.
54. The method of claim 51, wherein the function block operating parameter is a mode parameter and the step of collecting data includes the step of collecting mode indications for each of the multiplicity of function blocks.
55. The method of claim 54, wherein the step of determining includes the step of determining a mode value for one of the function blocks as a percentage of a specific period of time that the one of the function blocks was in one of a normal or a non-normal mode associated with the operation of the one of the function blocks and the step of detecting includes the step of comparing the mode value for the one of the function blocks to a mode limit.
56. The method of claim 51, wherein the function block operating parameter is a status parameter and the step of collecting data includes the step of collecting status indications for each of the multiplicity of function blocks.
57. The method of claim 56, wherein the step of determining includes the step of determining a status value for one of the function blocks as a percentage of a specific period of time that the status of the one of the function blocks was one of a normal status or a non-normal status associated with the one of the function blocks and the step of detecting includes the step of comparing the status value for the one of the function blocks to a status limit.
58. The method of claim 51, wherein the function block operating parameter is a limit parameter related to whether a function block variable is at one or more limits and the step of collecting data includes the step of collecting limit indications for each of the multiplicity of function blocks.
59. The method of claim 58, wherein the step of determining includes the step of determining a limit for one of the function blocks as a percentage of a specific period of time that the function block variable is at the one or more limits and the step of detecting includes the step of comparing the limit value for the one of the function blocks to a limit value limit.
60. The method of claim 51, further including the step of collecting an application state parameter from one of the multiplicity of function blocks and wherein the step of detecting includes the steps of ignoring the function block operating parameter data associated with the one of the multiplicity of function blocks to detect the problem when the function block operating parameter data is associated with a time in which the application state parameter was in a first state and using the function block operating parameter data associated with the one of the multiplicity of function blocks to detect the problem when the function block operating parameter data is associated with a time in which the application state parameter was in a second state.
61. The method of claim 51, further including the step of automatically recommending the use of a further tool to correct the one or more detected problems.
62. The method of claim 61, further including the step of automatically collecting process parameter data during operation of the process control system for use by the further recommended tool.
63. A function block for execution by a processor in a process control environment, comprising:
a computer readable memory;
a first routine stored on the computer readable memory and adapted to be executed on the processor to implement a process operation, wherein a first signal is associated with an operation of the first routine; and
a second routine stored on the computer readable memory and adapted to be executed on the processor to determine a variability indication based on the first signal.
64. The function block of claim 63, wherein the function block is an input function block, the first signal is indicative of a process parameter measurement and wherein the second routine compares a statistical measurement of the first signal to an instantaneous measurement of the first signal to determine the variability indication.
65. The function block of claim 64, wherein the statistical measurement of the first signal is a mean.
66. The function block of claim 63, wherein the function block is a control function block that uses a process parameter signal and a set point to produce a control signal and wherein the second routine compares the process parameter signal and the set point to determine the variability indication.
67. The function block of claim 63, wherein the variability indication is indicative of the integral squared error between the first signal and another value associated with the function block.
68. The function block of claim 63, wherein the second routine calculates a new value for the variability indication after each of a plurality of executions of the first routine.
69. The function block of claim 63, wherein the variability indication is based on a calculation of the mean absolute error of the first signal.
70. The function block of claim 63, wherein the variability indication is based on a calculation of a moving range average associated with the first signal.
71. The function block of claim 63, wherein the variability indication includes a first value representing a mean absolute error of first signal and a second value representing a moving range average of the first signal.
72. The function block of claim 63, wherein the variability indication includes a first variability value indicative of an actual total standard deviation associated with the first signal and a second variability value indicative of a capability standard deviation associated with the first signal.
73. The function block of claim 63, wherein the variability indication (V) is computed as
9
V
=

1
–
S

1

q
+
s
S
tot

+
s
wherein Slq is a minimum standard deviation expected with feedback control, Stot is an actual measured standard deviation and s is a sensitivity factor.
74. The function block of claim 63, wherein the function block is a Fieldbus function block and includes a communication unit that communicates the variability indication over a Fieldbus bus using a Fieldbus protocol.
75. A function block for execution on a processor as part of a process control application within a process control environment, comprising:
a computer readable memory;
a first routine stored on the computer readable memory and adapted to be executed on the processor to implement a process operation as part of the process control application; and
a state variable that is set to a first state when the process control application is using the function block in an attempt to perform the process operation and is set to a second state when the process control application is not using the function block in an attempt to perform the process operation.
76. The function block of claim 75, wherein the function block is a Fieldbus function block and includes a communication unit that communicates the state variable over a Fieldbus bus using a Fieldbus protocol.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

What is claimed is:

1. A semiconductor device,
wherein an oxide film for device isolation, formed on a region of a substrate where the effective thickness in the vertical direction of the substrate is decreased due to the presence of a defect is etched by a predetermined thickness until a recess ascribable to the presence of the defect is opposed, and
wherein subsequently a new oxide film is formed on the first-stated oxide film in an amount corresponding to said predetermined thickness.
2. The semiconductor device as defined in claim 1, wherein said predetermined thickness of etching off the oxide film for device isolation is set to a value which does not permit ion flight to a substrate portion directly below the oxide film for device isolation at the time of ion implantation for forming an impurity layer following the formation of the new oxide film on the first-stated oxide film in an amount corresponding to said predetermined thickness.
3. A method for producing a semiconductor device comprising:
etching off the surface of an oxide film for device isolation, formed on a region of a substrate where the effective thickness in the vertical direction of the substrate is decreased due to the presence of a defect, by a predetermined thickness, until a recess ascribable to the presence of the defect is exposed itself; and
newly forming an oxide film on the first-stated oxide film in an amount corresponding to said predetermined thickness to diminish the depth of the recess ascribable to the presence of the defect.
4. The method for producing a semiconductor device as defined in claim 3, wherein said predetermined thickness of etching off the oxide film for device isolation is set to a value which does not permit ion flight to a substrate portion directly below the oxide film for device isolation at the time of ion implantation for forming an impurity layer following the formation of the new oxide film on the first-stated oxide film in an amount corresponding to said predetermined thickness.
5. The method for producing a semiconductor device as defined in claim 3, wherein, when implanting ions for forming a channel stop in a portion of the substrate including the substrate portion directly below the oxide film for device isolation, ion implantation is carried out divisionally a plural number of times.
6. The method for producing a semiconductor device as defined in claim 4, wherein, when implanting ions for forming a channel stop in a portion of the substrate including the substrate portion directly below the oxide film for device isolation, ion implantation is carried out divisionally a plural number of times.
7. The method for producing a semiconductor device as defined in claim 5 wherein, when implanting ions for forming said channel stop, the ions are caused to travel beyond the maximum film thickness portion of the oxide film for device isolation at the time of initial ion implantation to form a first channel stop; and,
wherein for next or subsequent ion implantation, a second channel stop is formed to overlie a recess of said first channel stop in register with the recess of said oxide film for device isolation.
8. The method for producing a semiconductor device as defined in claim 6 wherein, when implanting ions for forming said channel stop, the ions are caused to travel beyond the maximum film thickness portion of the oxide film for device isolation at the time of initial ion implantation to form a first channel stop; and,
wherein for next or subsequent ion implantation, a second channel stop is formed to overlie a recess of said first channel stop in register with the recess of said oxide film for device isolation.
9. A method for producing a semiconductor device wherein the inter-lattice oxygen in a substrate is captured in a defect in said substrate by heat-treating said substrate.
10. A method for producing a semiconductor device comprising the steps of:
(a) sequentially forming an oxide film and a nitride film in a pre-set region of a substrate containing a defect therein;
(b) etching pre-set regions of said oxide film and said nitride film to form a pattern;
(c) forming an oxide film for device isolation using said oxide film and said nitride film as a mask; and
(d) implanting ions for forming a channel stop in a region of said substrate including a substrate region directly below said oxide film for device isolation;
(e) wherein an oxide film is formed on an inner side of said defect by high-temperature heat treatment prior to formation of said oxide film for device isolation.
11. The method for producing a semiconductor device as defined in claim 10 wherein said high-temperature heat treatment is performed before the step of forming said nitride film, following the step of forming said nitride film or following the formation of the pattern of said nitride film.
12. The method for producing a semiconductor device as defined in claim 11 wherein said high-temperature heat treatment is performed at a temperature of 1100 to 1200 C. for four to six hours.
13. A method for causing growth of a silicon crystal by the Czocralski method termed as Cz method,
wherein in a cooling process during uplifting of an ingot of said silicon crystal, the cooling rate is increased for a temperature range substantially from 1080 to 1150 C.
14. The method for producing a semiconductor device as defined in claim 13 wherein the cooling rate ranging approximately between 1080 to 1150 C. is set so that the size of the crystal defect produced in the silicon crystal will be not larger than 200 nm.
15. A semiconductor device prepared using a crystal manufactured by the method according to claim 13.
16. A semiconductor device prepared using a crystal manufactured by the method according to claim 14.
17. A semiconductor device, wherein a region amorphousized by implanting predetermined ions is provided in a portion of a region of a substrate destined for forming an oxide film for device separation where the effective thickness in the vertical direction of the substrate is decreased due to the presence of a defect.
18. The semiconductor device according to claim 17 wherein said ions comprise at least one selected from the group consisting of silicon, germanium, argon, boron and phosphorus ions.
19. A method for producing a semiconductor device comprising:
implanting predetermined ions in a portion of a substrate region destined for forming a thermally oxidized film to amorphize a substrate region, and further forming a thermally oxidized film subsequently, to provide an oxide film increased in thickness selectively in said amorphousized substrate region.
20. A method for producing a semiconductor device comprising:
implanting predetermined ions in a center region of a substrate destined for an oxide film for device isolation excluding an end portion destined to be a possible bird’s beak to amorphousize a substrate, and
subsequently forming the oxide film for device isolation to provide an increased oxide film thickness only in a center region of the oxide film for device isolation.
21. A method for producing a semiconductor device comprising the steps of:
(a) forming a nitride film in a pre-set region of a substrate containing a defect;
(b) etching said pre-set region of the nitride film to form a pattern of nitride film;
(c) depositing an oxide film to a pre-set thickness on the entire surface of the substrate;
(d) forming a sidewall oxide film by etching off said oxide film to permit the oxide film to remain only on an inner side of an opening of said patterned nitride film constituting a field oxide film;
(e) implanting predetermined ions, using said nitride film and the sidewall oxide film as a mask, to amorphousize an exposed portion of the substrate;
(f) etching off said sidewall oxide film; and
(g) forming an oxide film for device isolation by forming an oxide film, using said nitride film as a mask, so that the oxide film is increased in film thickness in said amorphousized portion.
22. The method for producing a semiconductor device as defined in claim 20 wherein the film thickness of the portion of the oxide film for device isolation corresponding to the amorphousized portion is set to a value which does not permit ions to travel to a substrate portion directly below the oxide film for device isolation at the time of ion implantation for forming an impurity layer.
23. The method for producing a semiconductor device as defined in claim 21 wherein the film thickness of the portion of the oxide film for device isolation corresponding to the amorphousized portion is set to a value which does not permit ions to travel to a substrate portion directly below the oxide film for device isolation at the time of ion implantation for forming an impurity layer.
24. The method for producing a semiconductor device as defined in claim 19 wherein said ions comprise at least one selected from the group consisting silicon, germanium, argon, boron and phosphorus ions.
25. The method for producing a semiconductor device as defined in claim 20 wherein said ions comprise at least one selected from the group consisting silicon, germanium, argon, boron and phosphorus ions.
26. The method for producing a semiconductor device as defined in claim 21 wherein said ions comprise at least one selected from the group consisting silicon, germanium, argon, boron and phosphorus ions.