WO2020029141A1 - Procédé et dispositif de modélisation pour générateur de nombres aléatoires, et support et générateur de nombres aléatoires - Google Patents

Procédé et dispositif de modélisation pour générateur de nombres aléatoires, et support et générateur de nombres aléatoires Download PDF

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Publication number
WO2020029141A1
WO2020029141A1 PCT/CN2018/099469 CN2018099469W WO2020029141A1 WO 2020029141 A1 WO2020029141 A1 WO 2020029141A1 CN 2018099469 W CN2018099469 W CN 2018099469W WO 2020029141 A1 WO2020029141 A1 WO 2020029141A1
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random number
number generator
inverters
architecture
oscillation
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PCT/CN2018/099469
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English (en)
Chinese (zh)
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韦健
陈建兴
王冬格
申艾麟
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深圳市汇顶科技股份有限公司
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Priority to CN201880001134.1A priority Critical patent/CN111010880A/zh
Priority to PCT/CN2018/099469 priority patent/WO2020029141A1/fr
Publication of WO2020029141A1 publication Critical patent/WO2020029141A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/58—Random or pseudo-random number generators

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  • the present application relates to the field of computer technology, and in particular, to a method, a device, a medium, and a random number generator for modeling a random number generator.
  • Random number generators are usually used to generate random numbers. According to different generation methods, random number generators can be divided into pseudo-random number generators and true random number generators. Among them, the true random number generator uses the random characteristics of physical phenomena to generate true random numbers. It is necessary to generate non-deterministic, non-periodic, non-copyable random entropy primitives. At present, a true random number generator can be implemented by an analog circuit or a digital circuit. The implementation of digital circuits can design high-quality entropy, so digital circuits are the development trend of random number generators. However, the random number generator implemented by digital circuits needs to determine the architecture of the random number generator in the early design stage. In the prior art, a framework for generating a random number generator by human experience is relied on, which will inevitably cause a problem of low reliability of the random number generator.
  • the application provides a modeling method, device, medium, and random number generator for a random number generator, thereby improving the reliability of the random number generator.
  • the present application provides a method for modeling a random number generator, including: generating a current architecture of the random number generator; simulating the current architecture of the random number generator to obtain a spectrum simulation result; if the spectrum simulation result satisfies The preset conditions determine the final structure of the random number generator based on the current structure of the random number generator; if the spectrum simulation results do not meet the preset conditions, the current structure of the random number generator is adjusted, and the random number generator is adjusted after The simulation of the architecture is performed to obtain the spectrum simulation results, until the obtained spectrum simulation results meet the preset conditions.
  • the final architecture of the random number generator can be determined by this method.
  • the spectrum simulation results corresponding to the final architecture of the random number generator meet preset conditions. Based on this, the random number generator generated by this method is more reliable.
  • determining the final architecture of the random number generator according to the current architecture of the random number generator includes: generating a random number through the current architecture of the random number generator; and determining the random number generator if the quality of the random number satisfies a test condition.
  • the current architecture of is the final architecture of the random number generator; if the quality of the random number does not meet the test conditions, adjust the current architecture of the random number generator and generate random numbers through the adjusted current architecture until the quality of the random number meets the test So far.
  • the final architecture of the random number generator can be effectively determined by this method.
  • the current architecture includes: adjusting at least one of M, N, P, and Q in the current architecture of the random number generator.
  • the equivalent number of inverters included in any two of the M oscillation rings is mutually prime; wherein, the equivalent number of inverters in any of the oscillation rings is equal to that of the inverters in any of the oscillation rings.
  • the actual number is equal to the sum of the number of inverters in any oscillating ring and the number of multiplexers in any oscillating ring.
  • the current architecture of generating a random number generator includes: determining the delays of the NAND gate, the inverter, and the multiplexer, respectively; and determining the AND based on the delays of the NAND gate, the inverter, and the multiplexer, respectively.
  • This method can effectively generate the current architecture of the random number generator.
  • the preset condition is at least one of the following: the spectrum simulation result is a high-frequency spectrum, the spectrum simulation result is non-correlated, and the spectrum simulation result is uniform.
  • the modeling device, medium, computer program product, and random number generator of the random number generator will be introduced below.
  • the effects please refer to the effects of the method part, which will not be described in detail below.
  • the present application provides a modeling device for a random number generator, including:
  • the simulation module is used to simulate the current architecture of the random number generator and obtain the spectrum simulation results.
  • a determining module configured to determine a final structure of the random number generator according to a current structure of the random number generator if the spectrum simulation result meets a preset condition.
  • An adjustment module for adjusting the current architecture of the random number generator if the spectrum simulation result does not satisfy a preset condition, and simulating the adjusted architecture of the random number generator to obtain a spectrum simulation result until the obtained spectrum simulation result satisfies Up to the preset conditions.
  • the determining module is specifically configured to: generate a random number through the current architecture of the random number generator; if the quality of the random number meets the test conditions, determine that the current architecture of the random number generator is the final architecture of the random number generator; The quality of the random number does not meet the test conditions, then adjust the current architecture of the random number generator, and generate random numbers through the adjusted current architecture until the quality of the random numbers meets the test conditions.
  • the equivalent number of inverters included in any two of the M oscillation rings is mutually prime; wherein, the equivalent number of inverters in any of the oscillation rings is equal to that of the inverters in any of the oscillation rings.
  • the actual number is equal to the sum of the number of inverters in any oscillating ring and the number of multiplexers in any oscillating ring.
  • the generating module is specifically configured to determine the delays of the NAND gate, the inverter, and the multiplexer, respectively; and determine the NAND gate, the inversion, respectively, according to the delays of the NAND gate, the inverter, and the multiplexer.
  • the preset condition is at least one of the following: the spectrum simulation result is a high-frequency spectrum, the spectrum simulation result is non-correlated, and the spectrum simulation result is uniform.
  • the present application provides a modeling device for a random number generator, including: a processor and a memory, where the memory is configured to store execution instructions of the processor, so that the processor implements the first aspect or the first aspect of the present invention. Election method.
  • the present application provides a random number generator generated according to the foregoing first aspect or the method of the first aspect, including: K oscillation rings connected in parallel and an XOR circuit, K oscillations The output end of the ring is connected to the input end of the XOR gate.
  • the oscillating ring includes: an enable control module, a test control module, a test control module, an inverter chain module, a loop length control module, and a loop length control module in series. An output terminal is connected to an input terminal of the enable control module.
  • the enable control module is used to receive an enable signal through an input terminal. When the enable signal is 1, the oscillator is controlled to work.
  • the test control module is configured to receive an enable signal through one input terminal and a test input signal through the other input terminal to test whether the oscillation ring is stuck according to the enable signal and the test input signal of the enable control module.
  • the inverter chain module is used to invert the phase of the output signal of the test control module to obtain the output signal of the oscillation ring.
  • Loop length control module for controlling the length of the inverter chain module.
  • the test control module is specifically configured to: if the output signal of the enable control module is 0, the test input signal is 1, and the output signal of the oscillation ring is 0, it is determined that the oscillation ring is in a normal state; if the control module is enabled The output signal of is 0, the test input signal is 1, and the output signal of the oscillating ring is 1, it is determined that the oscillating ring is stuck and cannot vibrate.
  • the enable control module includes: a two-input NAND gate
  • the test control module includes: a two-input NAND gate
  • the inverter chain module includes: an odd number of inverters connected in series
  • the loop length control module includes: A multiplexer
  • the equivalent number of inverters included in any two of the K oscillation rings is mutually prime; wherein, the equivalent number of inverters in any of the oscillation rings is equal to that of the inverters in any of the oscillation rings.
  • the actual number is equal to the sum of the number of inverters in any oscillating ring and the number of multiplexers in any oscillating ring.
  • each of the K oscillation rings receives the same enable signal.
  • the present application provides a computer storage medium.
  • the computer storage medium includes instructions, and the instructions are used to implement the method in the first aspect or an optional manner of the first aspect.
  • the present application provides a computer program product.
  • the computer program product includes instructions, and the instructions are used to implement the method in the first aspect or an optional manner of the first aspect.
  • the application provides a modeling method, device, medium and random number generator for a random number generator including: generating a current architecture of the random number generator; simulating the current architecture of the random number generator to obtain a spectrum simulation result; If the spectrum simulation result meets the preset conditions, the final structure of the random number generator is determined according to the current structure of the random number generator. If the spectrum simulation result does not meet the preset conditions, the current structure of the random number generator is adjusted, and the random number is adjusted. The adjusted architecture of the generator is simulated to obtain the spectrum simulation results, until the obtained spectrum simulation results meet the preset conditions. The final architecture of the random number generator can be determined by this method. The spectrum simulation results corresponding to the final architecture of the random number generator meet preset conditions. Based on this, the random number generator generated by this method has higher reliability.
  • FIG. 1 is a flowchart of a method for modeling a random number generator according to an embodiment of the present application
  • FIG. 2 is a flowchart of a method for generating a current architecture of a random number generator according to an embodiment of the present application
  • FIG. 3 is a schematic diagram of a current architecture of a random number generator according to an embodiment of the present application.
  • FIG. 4 is a flowchart of a method for determining a final architecture of a random number generator according to an embodiment of the present application
  • FIG. 5 is a schematic diagram of a random number generator according to an embodiment of the present application.
  • FIG. 6 is a schematic diagram of a random number generator according to another embodiment of the present application.
  • FIG. 7 is a schematic diagram of a modeling device 70 for a random number generator according to an embodiment of the present application.
  • FIG. 8 is a schematic diagram of a modeling device 80 for a random number generator according to an embodiment of the present application.
  • random number generators are usually used to generate random numbers.
  • random number generators can be divided into pseudo-random number generators and true random number generators.
  • the pseudo-random number generator needs to provide a random number seed, that is, the initial value, to generate a certain random number sequence by a deterministic algorithm.
  • the seed input as the pseudo-random number must be safe, and the sequence itself will inevitably repeat.
  • the safety of the generator is no longer sufficient to meet specific safety requirements.
  • the true random number generator uses the random characteristics of physical phenomena to generate true random numbers. It needs to generate non-deterministic, non-periodic, non-replicatable random entropy primitives.
  • a true random number generator can be implemented by an analog circuit or a digital circuit.
  • the implementation of digital circuits can design high-quality entropy primitives, so digital circuits are the development trend of random number generators.
  • the random number generator implemented by digital circuits needs to determine the architecture of the random number generator in the early design stage.
  • a framework for generating a random number generator by human experience is relied on, which will inevitably cause a problem of low reliability of the random number generator.
  • the present application provides a modeling method, device, medium, and random number generator for a random number generator.
  • the technical solution provided in this application can be applied to any scenario that requires data encryption.
  • FIG. 1 is a flowchart of a method for modeling a random number generator according to an embodiment of the present application.
  • the execution subject of the method may be a part or all of a smart terminal such as a computer, a personal computer (PC), or a mobile phone.
  • the application uses the computer as an example to explain the method. As shown in FIG. 1, the method includes the following steps:
  • Step S101 Generate the current architecture of the random number generator.
  • Step S102 The current architecture of the random number generator is simulated to obtain a spectrum simulation result.
  • Step S103 if the spectrum simulation result meets a preset condition, determine the final architecture of the random number generator according to the current architecture of the random number generator.
  • Step S104 if the spectrum simulation result does not satisfy the preset conditions, adjust the current architecture of the random number generator, and simulate the adjusted architecture of the random number generator to obtain the spectrum simulation result, until the obtained spectrum simulation result meets the preset So far.
  • FIG. 2 is a flowchart of a method for generating a current architecture of a random number generator according to an embodiment of the present application. As shown in FIG. 2, the method includes:
  • Step S201 Determine the delays of the NAND gate, the inverter, and the multiplexer, respectively.
  • Step S202 Determine the number of the NAND gate, the inverter and the multiplexer according to the delays of the NAND gate, the inverter and the multiplexer, respectively.
  • Step S203 Generate the current architecture of the random number generator according to the number of the NAND gate, the inverter and the multiplexer.
  • the computer can obtain the delay corresponding to each device from the process library file in advance, including the delay of the inverter, the delay of the NAND gate, and the delay of the multiplexer. Wait.
  • the computer can obtain the delay range allowed by the final architecture of the random number generator formed by modeling, and determine the number of each device according to the delay corresponding to each device and the delay range allowed by the final architecture of the random number generator, such as :
  • the delay range allowed by the final architecture of the random number generator is [A, B].
  • the delay range may be the default delay range. Assume that there are x inverters, y NAND gates, and z multiplexers, and the delay of the inverter is a, the delay of the NAND gate is b, and the delay of the multiplexer is c. The number satisfies the following formula:
  • the computer can convert the delay of each device into the delay of the inverter.
  • the converted delay is called the equivalent delay
  • the mapping relationship can be stored in the form of a table. Based on this, the number of corresponding inverters can be determined by looking up the table according to the equivalent delay of each device, and then according to the equivalent relationship between the inverter and each device. , Determine the number of each device.
  • FIG. 3 is a schematic diagram of the current architecture of the random number generator provided by an embodiment of the present application, as shown in FIG. 3.
  • the current architecture includes: M oscillation rings 31 connected in parallel and an XOR gate circuit 32. The output ends of the M oscillation rings 31 are connected to the input terminals of the XOR gate 32.
  • the equivalent number of inverters included in any two of the M oscillation rings is mutually prime; wherein, the equivalent number of inverters in any oscillation ring is the actual number of inverters in any oscillation ring
  • the AND gate in any oscillation ring is equivalent to the sum of the number of inverters, and the multiplexer in any oscillation ring is equivalent to the sum of the number of inverters. In this way, non-correlation can be guaranteed for spectrum simulation results.
  • the computer may also obtain a frequency range of a frequency spectrum required by the final architecture of the random number generator formed by modeling.
  • the number of each device is determined according to the frequency range required by the final architecture of the random number generator.
  • the frequency range required by the final architecture of the random number generator is [C, D].
  • the frequency range may be Is the default frequency range.
  • the multiplexer The frequency of the corresponding formed spectrum is f, then the number of each device satisfies the following formula:
  • the computer may convert the frequency of the frequency spectrum corresponding to each device to the frequency of the frequency spectrum corresponding to the inverter.
  • the converted frequency is referred to as the equivalent frequency
  • the equivalent frequency and the frequency of the inverter There is a mapping relationship between the numbers.
  • the mapping relationship can be stored in the form of a table. Based on this, the number of corresponding inverters can be determined by looking up the table according to the equivalent frequency of each device. The equivalent relationship between each device determines the number of each device.
  • the current architecture of the random number generator can be designed as follows, for example, the current architecture of the random number generator shown in FIG. 3.
  • the equivalent number of inverters included in any two of the M oscillation rings is mutually prime; wherein, the equivalent number of inverters in any oscillation ring is the actual number of inverters in any oscillation ring
  • the AND gate in any oscillation ring is equivalent to the sum of the number of inverters
  • the multiplexer in any oscillation ring is equivalent to the sum of the number of inverters.
  • Steps S102 to S104 are described: SPICE simulation is performed on the current architecture of the random number generator to obtain a spectrum simulation result. If the spectrum simulation result meets a preset condition, the final architecture of the random number generator is determined according to the current architecture of the random number generator. If the spectrum simulation results do not satisfy the preset conditions, the current architecture of the random number generator is adjusted, and the adjusted architecture of the random number generator is simulated to obtain the spectrum simulation results until the obtained spectrum simulation results meet the preset conditions.
  • the preset condition is at least one of the following: the spectrum simulation result is a high-frequency spectrum, the spectrum simulation result is non-correlated, and the spectrum simulation result is uniform.
  • step S103 the final architecture of the random number generator is determined according to the current architecture of the random number generator, which includes at least the following two optional methods:
  • the current architecture of the random number generator is determined as the final architecture of the random number generator.
  • FIG. 4 is a flowchart of a method for determining a final architecture of a random number generator according to an embodiment of the present application. As shown in FIG. 4, the method includes the following steps:
  • Step S401 Generate a random number through the current architecture of the random number generator.
  • Step S402 if the quality of the random number satisfies the test condition, determine that the current architecture of the random number generator is the final architecture of the random number generator.
  • Step S403 if the quality of the random number does not satisfy the test condition, adjust the current architecture of the random number generator, and generate a random number through the adjusted current architecture until the quality of the random number meets the test condition.
  • the computer can perform the NIST800-22 standard test on the current architecture of the random number generator.
  • the NIST800-22 standard test includes multiple test conditions. If the quality of the random number meets the test conditions, the current architecture of the random number generator is determined. The final architecture of the random number generator. If the quality of the random number does not satisfy the test condition, the current architecture of the random number generator is adjusted, and the random number is generated by the adjusted current architecture until the quality of the random number meets the test condition.
  • the current architecture includes: adjusting at least one of M, N, P, and Q in the current architecture of the random number generator.
  • the present application provides a method for modeling a random number generator, by which the final architecture of the random number generator can be determined, wherein the spectrum simulation result corresponding to the final architecture of the random number generator meets a preset condition, such as the spectrum simulation result For high-frequency spectrum, the spectrum simulation results are non-correlated and the spectrum simulation results are uniform. Based on this, the random number generator generated by this method is more reliable.
  • FIG. 5 is a schematic diagram of a random number generator according to an embodiment of the present application.
  • the random number generator is generated by using the above-mentioned modeling method of the random number generator.
  • the random number generator includes K oscillation rings 51 and an XOR circuit 52 connected in parallel.
  • the output terminals of the K oscillation rings 51 are connected to the input terminals of the XOR gate 52, and the oscillation ring 51 Including: an enable control module 511, a test control module 512, an inverter chain module 513 and a loop length control module 514 in series, and an output terminal of the loop length control module 514 and an input terminal of the enable control module 511 connection.
  • the enable control module 511 is used to receive an enable signal through an input terminal. When the enable signal is 1, the oscillator control loop is controlled to work.
  • the test control module 512 is used to receive the enable signal of the enable control module through one input terminal and the test input signal through the other input terminal to test whether the oscillating ring is in accordance with the enable signal and the test input signal of the enable control module 511. Stuck.
  • the inverter chain module 513 is configured to perform phase inversion on the output signal of the test control module 512 to obtain an output signal of an oscillation ring.
  • the loop length control module 514 is used to control the length of the inverter chain module 513.
  • the test control module 512 is specifically configured to: if the output signal of the enable control module 511 is 0, the test input signal is 1, and the output signal of the oscillation ring is 0, it is determined that the oscillation ring is in a normal state; if enabled The output signal of the control module 511 is 0, the test input signal is 1, and the output signal of the oscillating ring is 1, it is determined that the oscillating ring is in a stuck state and cannot self-vibrate.
  • FIG. 6 is a schematic diagram of a random number generator provided by another embodiment of the present application.
  • the enable control module includes: a two-input NAND gate 61
  • the test control module includes: a two-input The NAND gate 62
  • the inverter chain module includes: an odd number of inverters 63 connected in series
  • the loop length control module includes: a multiplexer 64, wherein the multiplexer has two input terminals, 0 and 0 respectively. 1.
  • Input terminal 0 is connected to terminal a in the inverter chain module
  • input terminal 1 is connected to terminal b in the inverter chain module. This connection is used to control the length of the inverter chain module.
  • the equivalent number of inverters included in any two of the K oscillation rings is mutually prime; wherein, the equivalent number of inverters in any of the oscillation rings is equal to that of the inverters in any of the oscillation rings.
  • the actual number is equal to the sum of the number of inverters in the oscillating ring and the number of inverters in the oscillating ring.
  • each of the K oscillation rings receives respective corresponding enable signals, that is, there are K enable signals corresponding to the K oscillation rings, or the K oscillation rings receive the same enable signal, and Or part of the oscillating rings receive the same enable signal, and the other part of the oscillating rings receive the corresponding enable signals.
  • K1 oscillating rings in the K oscillating rings receive the same enabling signal
  • K2 oscillating rings receive the corresponding enabling signals.
  • the enable signal is 0 or 1, which is not limited in this application.
  • the present application provides a random number generator.
  • the random number generator is generated by the above-mentioned modeling method of the random number generator, so the reliability of the random number generator is higher. Further, the test control module in the random number generator can detect whether the random number generator is in a stuck state, further improving the reliability of the random number generator.
  • FIG. 7 is a schematic diagram of a random number generator modeling device 70 provided by an embodiment of the present application. As shown in FIG. 7, the device may be part or all of a smart terminal such as a computer, a PC, or a mobile phone. :
  • the generating module 71 is configured to generate a current architecture of the random number generator.
  • the simulation module 72 is used for simulating the current architecture of the random number generator to obtain a spectrum simulation result.
  • the determining module 73 is configured to determine a final structure of the random number generator according to a current structure of the random number generator if the spectrum simulation result meets a preset condition.
  • the adjustment module 74 is used to adjust the current architecture of the random number generator if the spectrum simulation results do not meet the preset conditions, and simulate the adjusted architecture of the random number generator to obtain the spectrum simulation results until the obtained spectrum simulation results meet Up to the preset conditions.
  • the determining module 73 is specifically configured to: generate a random number through the current architecture of the random number generator; if the quality of the random number meets the test conditions, determine that the current architecture of the random number generator is the final architecture of the random number generator; If the quality of the random number does not satisfy the test condition, the current architecture of the random number generator is adjusted, and the random number is generated by the adjusted current architecture until the quality of the random number meets the test condition.
  • the equivalent number of inverters included in any two of the M oscillation rings is mutually prime; wherein, the equivalent number of inverters in any of the oscillation rings is equal to that of the inverters in any of the oscillation rings.
  • the actual number is equal to the sum of the number of inverters in any oscillating ring and the number of multiplexers in any oscillating ring.
  • the generating module 71 is specifically configured to: determine the delays of the NAND gate, the inverter, and the multiplexer, respectively; and determine the NAND gates, the inverters, and the delay according to the delays of the NAND gate, the inverter, and the multiplexer, respectively.
  • the preset condition is at least one of the following: the spectrum simulation result is a high-frequency spectrum, the spectrum simulation result is non-correlated, and the spectrum simulation result is uniform.
  • the modeling device of the random number generator provided in this application is used to execute the above-mentioned modeling method of the random number generator, and its content and effects are not described herein again.
  • FIG. 8 is a schematic diagram of a random number generator modeling device 80 according to an embodiment of the present application.
  • the device may be a smart terminal such as a computer, a PC, or a mobile phone.
  • the device includes a processor 81 And a memory 82.
  • the memory 82 is configured to store execution instructions of the processor, so that the processor implements 81 the modeling method of the random number generator described above.
  • the device 80 further includes a transceiver 83, which can implement communication between the modeling device 80 of the random number generator and other devices.
  • the modeling device of the random number generator provided in this application is used to execute the above-mentioned modeling method of the random number generator, and its content and effects are not described herein again.
  • An embodiment of the present application provides a computer storage medium.
  • the computer storage medium includes instructions, and the instructions are used to implement the modeling method of the random number generator described above, and the contents and effects thereof are not described herein again.
  • a person of ordinary skill in the art may understand that all or part of the steps of implementing the foregoing method embodiments may be implemented by a program instructing related hardware.
  • the aforementioned program may be stored in a computer-readable storage medium.
  • the steps including the foregoing method embodiments are executed; and the foregoing storage medium includes: various media that can store program codes, such as a ROM, a RAM, a magnetic disk, or an optical disc.

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Abstract

L'invention concerne un procédé et un dispositif de modélisation pour un générateur de nombres aléatoires, ainsi qu'un support et un générateur de nombres aléatoires. Le procédé consiste à : générer une architecture actuelle d'un générateur de nombres aléatoires; simuler l'architecture actuelle du générateur de nombres aléatoires de façon à obtenir un résultat de simulation de spectre; si le résultat de simulation de spectre satisfait à une condition prédéfinie, déterminer une architecture finale du générateur de nombres aléatoires en fonction de l'architecture actuelle du générateur de nombres aléatoires; et si le résultat de simulation de spectre ne satisfait pas à la condition prédéfinie, ajuster l'architecture actuelle du générateur de nombres aléatoires, et simuler l'architecture ajustée du générateur de nombres aléatoires pour obtenir un résultat de simulation de spectre jusqu'à ce que le résultat de simulation de spectre obtenu satisfasse à la condition prédéfinie. Un générateur de nombres aléatoires généré par ce procédé est plus fiable.
PCT/CN2018/099469 2018-08-08 2018-08-08 Procédé et dispositif de modélisation pour générateur de nombres aléatoires, et support et générateur de nombres aléatoires WO2020029141A1 (fr)

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CN201880001134.1A CN111010880A (zh) 2018-08-08 2018-08-08 随机数发生器的建模方法、装置、介质及随机数发生器
PCT/CN2018/099469 WO2020029141A1 (fr) 2018-08-08 2018-08-08 Procédé et dispositif de modélisation pour générateur de nombres aléatoires, et support et générateur de nombres aléatoires

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