JPH07191732A - Method and device for deciding process specification in production process of product - Google Patents

Method and device for deciding process specification in production process of product

Info

Publication number
JPH07191732A
JPH07191732A JP33095493A JP33095493A JPH07191732A JP H07191732 A JPH07191732 A JP H07191732A JP 33095493 A JP33095493 A JP 33095493A JP 33095493 A JP33095493 A JP 33095493A JP H07191732 A JPH07191732 A JP H07191732A
Authority
JP
Japan
Prior art keywords
product
particles
evaluation function
manufacturing process
relationship
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
JP33095493A
Other languages
Japanese (ja)
Inventor
Hiroichi Ueda
博一 上田
Kayako Oomura
佳也子 大村
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Kobe Steel Ltd
Original Assignee
Kobe Steel Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Kobe Steel Ltd filed Critical Kobe Steel Ltd
Priority to JP33095493A priority Critical patent/JPH07191732A/en
Publication of JPH07191732A publication Critical patent/JPH07191732A/en
Pending legal-status Critical Current

Links

Classifications

    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02PCLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02PCLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30Computing systems specially adapted for manufacturing

Landscapes

  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • General Factory Administration (AREA)
  • Control By Computers (AREA)

Abstract

PURPOSE:To maximize nondefective product productivity by quantitatively evalulating both the yield improving effect by the maintenance of a producing equipment and the lowering of productivity caused by downtime to decide the particle control specs of a process. CONSTITUTION:This device is provided with a no.1 storage part 1 previously storing a first relation expressing the corresponding relation between the generation amount of particles generated at the production process of the product and the nondefective product rate of the product, which is hindered by the particles. Furthermore, the device is provided with a no.2 storage part 2 previously storing a second relation expressing the corresponding relation between the frequency of the maintenance of the production process for reducing the particles and the production amount of the product. Then, an evaluation function arithmetic part 3 calculates an evaluation function for the productivity evaluation of the production process through the use of the first and second relations stored in the respective no.1 and 2 storage parts 1 and 2. Next, a process specs deciding part 4 decides the process specs being the allowable value of the particles in the production process based on the evaluation function calculated by the evaluation function arithmetic part 3.

Description

【発明の詳細な説明】Detailed Description of the Invention

【0001】[0001]

【産業上の利用分野】本発明は,製品の製造工程におけ
る工程スペック決定方法及びその装置に係り,例えば超
精密機械や集積回路等の製品の製造工程における工程ス
ペック決定方法及びその装置に関するものである。
BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a process specification determining method and apparatus in a product manufacturing process, and more particularly to a process specification determining method and apparatus in a product manufacturing process such as ultra-precision machines and integrated circuits. is there.

【0002】[0002]

【従来の技術】一般に製品を製造する場合,製品の製造
工程における工程スペックは,製品を製造する前段階で
ある設計段階で,必要とされる加工寸法,処理温度等の
既知の許容値を検討した上でトップダウンにて仮決定さ
れる。この仮の工程スペックは,理論的に机上で試算さ
れた値である。次に,この試算値に基づいて良品が製造
できるか否かを実際に実験で検証する。検証結果が否定
的であれば,人間が上記仮の工程スペックを修正し再検
証するといった手順を繰り返す。そして,良品が製造で
きることを検証できれば,上記仮の工程スペックは,製
造工程における正規の工程スペックとなる。従来は,上
記のごとく,初めに机上で必要とされる既知の許容値を
基に工程スペックを仮決定し,その妥当性を検討する工
程スペック決定方法が用いられていた。
2. Description of the Related Art Generally, when manufacturing a product, the process specification in the manufacturing process of the product is to consider the known allowable values such as processing dimensions and processing temperature in the design stage which is a stage before manufacturing the product. After that, it will be tentatively decided from the top down. This tentative process specification is a theoretical theoretically calculated value. Next, based on this trial calculation value, it is actually verified by experiments whether a good product can be manufactured. If the verification result is negative, the human repeats the procedure of correcting and re-verifying the temporary process specifications. Then, if it is possible to verify that a non-defective product can be manufactured, the above-mentioned temporary process specifications become regular process specifications in the manufacturing process. Conventionally, as described above, a process specification determination method has been used in which a process specification is first provisionally determined based on a known allowable value required on a desk and its validity is examined.

【0003】[0003]

【発明が解決しようとする課題】上記したような従来の
製品の製造工程における工程スペック決定方法では,次
のような問題があった。近年急速な発達をした新しい超
精密機械や集積回路,HDD(ハードディスクドライ
ブ),液晶ディスプレイ,CCD(チャージカップルド
デバイス)等の精密機械や電子部品等の製品の製造で
は,製造装置内に浮遊するパーティクル(ダスト)が製
造中の製品に付着することにより製品の良品率を低下さ
せることがわかっている。この製造装置内に浮遊するパ
ーティクルを低減させるためには,稼働中の製造装置の
運転を一時停止し,人手による装置クリーニングをする
必要がある。この作業は,製品の良品率を向上させる
が,一方では製造装置の稼働率を低下させ,その結果,
製品の生産量を低下させる。現状では製造装置内に浮遊
するパーティクルの製品への付着量の測定は可能ではあ
るものの,パーティクルをどの程度まで低減させれば,
良品を生産するのに充分であるのか,また,メンテナン
ス作業の頻度は生産量を極力低下させないという観点か
らどの程度が適当であるのかということについてはよく
わかっていない。このため,従来の工程スペック決定方
法では,パーティクルの製品への付着についてのスペッ
クを決定することができず,その管理を充分になし得な
かった。本発明は,製造装置のメンテナンスによる良品
率(歩留り)の向上とメンテナンスによるダウンタイム
に伴う生産性の低下との両方を考慮して,製造装置全体
で良品の生産性を最大化するように製造工程のパーティ
クル管理スペックを決定し得る製品の製造工程における
工程スペック決定方法及びその装置を提供することを目
的とするものである。
The above-described conventional process specification determining method in the manufacturing process of products has the following problems. In the manufacture of precision machines such as new ultra-precision machines and integrated circuits, HDDs (hard disk drives), liquid crystal displays, CCDs (charge coupled devices), and electronic parts that have rapidly developed in recent years, they float in manufacturing equipment. It has been found that particles (dust) adhere to the product being manufactured, thereby reducing the yield rate of the product. In order to reduce the particles floating in the manufacturing apparatus, it is necessary to suspend the operation of the manufacturing apparatus in operation and manually clean the apparatus. This work improves the yield rate of products, but on the other hand reduces the operating rate of manufacturing equipment, resulting in
Reduce the production of products. At present, it is possible to measure the amount of particles floating in the manufacturing equipment attached to the product, but to what extent can the particles be reduced?
It is not well known whether it is sufficient to produce a good product, and how appropriate the maintenance work frequency is from the viewpoint of minimizing the production volume. For this reason, the conventional process specification determination method cannot determine the specification regarding the adhesion of particles to the product, and the control thereof cannot be sufficiently performed. The present invention is designed to maximize the productivity of non-defective products in the entire manufacturing device in consideration of both the improvement of the non-defective product rate (yield) due to the maintenance of the manufacturing device and the decrease in productivity due to downtime due to the maintenance. An object of the present invention is to provide a process specification determining method and apparatus in a manufacturing process of a product capable of determining a particle management specification of a process.

【0004】[0004]

【課題を解決するための手段】上記目的を達成するため
に本発明は,製品の製造工程で発生するパーティクルの
発生量と該パーティクルによって阻害される上記製品の
歩留りとの対応関係を表す第1の関係,及び上記パーテ
ィクルを低減するための上記製造工程のメンテナンスの
頻度と上記製品の生産量との対応関係を表す第2の関係
を予め記憶しておき,上記記憶された第1,第2の関係
を用いて上記製造工程の生産性を評価するための評価関
数を演算し,上記演算された評価関数に基づいて上記製
造工程におけるパーティクルの発生量の許容値である工
程スペックを決定してなる製品の製造工程における工程
スペック決定方法として構成されている。また,製品の
製造工程で発生するパーティクルの発生量と該パーティ
クルによって阻害される上記製品の歩留りとの対応関係
を表す第1の関係を予め記憶しておく第1の記憶手段
と,上記パーティクルを低減するための上記製造工程の
メンテナンスの頻度と上記製品の生産量との対応関係を
表す第2の関係を予め記憶しておく第2の記憶手段と,
上記第1,第2の記憶手段にそれぞれ記憶された第1,
第2の関係を用いて上記製造工程の生産性を評価するた
めの評価関数を演算する評価関数演算手段と,上記評価
関数演算手段により演算された評価関数に基づいて上記
製造工程におけるパーティクルの発生量の許容値である
工程スペックを決定する工程スペック決定手段とを具備
してなる製品の製造工程における工程スペック決定装置
である。
In order to achieve the above-mentioned object, the present invention provides a first relation representing the correspondence between the amount of particles generated in the manufacturing process of a product and the yield of the product which is hindered by the particles. And the second relationship representing the correspondence relationship between the maintenance frequency of the manufacturing process for reducing the particles and the production amount of the product are stored in advance, and the stored first and second relationships are stored. The evaluation function for evaluating the productivity of the manufacturing process is calculated using the relationship of, and the process specification, which is the allowable value of the amount of particles generated in the manufacturing process, is determined based on the calculated evaluation function. It is configured as a process specification determination method in the manufacturing process of the following products. In addition, a first storage unit that stores in advance a first relationship that represents a correspondence relationship between the amount of particles generated in the manufacturing process of the product and the yield of the product that is hindered by the particles; Second storage means for storing in advance a second relationship representing a correspondence relationship between the frequency of maintenance of the manufacturing process for reducing and the production amount of the product;
The first and the first stored in the first and second storage means, respectively.
Generation of particles in the manufacturing process based on an evaluation function calculating unit that calculates an evaluation function for evaluating the productivity of the manufacturing process using the second relationship, and the evaluation function calculated by the evaluation function calculating unit. A process specification determining device in a manufacturing process of a product, comprising: a process specification determining unit that determines a process specification that is an allowable value of quantity.

【0005】[0005]

【作用】本発明によれば,製品の製造工程で発生するパ
ーティクルの発生量と該パーティクルによって阻害され
る上記製品の歩留りとの対応関係を示す第1の関係,及
び上記パーティクルを低減するための上記製造工程のメ
ンテナンスの頻度と上記製品の生産量との対応関係を表
す第2の関係が予め記憶される。上記記憶された第1,
第2の関係を用いて上記製造工程の生産性を評価するた
めの評価関数が演算される。上記演算された評価関数に
基づいて上記製造工程におけるパーティクルの発生量の
許容値である工程スペックが決定される。このように,
製造装置のメンテナンスによる歩留りの向上効果と該メ
ンテナンスによるダウンタイムに伴う生産性の低下との
両方を定量的に評価することにより,製造装置全体で
の,良品の生産性を最大化するような製造工程のパーテ
ィクル管理スペックを決定することが可能となる。
According to the present invention, the first relationship showing the correspondence relationship between the amount of particles generated in the manufacturing process of a product and the yield of the product hindered by the particles, and the above-mentioned relationship for reducing the particles A second relationship representing a correspondence relationship between the maintenance frequency of the manufacturing process and the production amount of the product is stored in advance. The first stored above
An evaluation function for evaluating the productivity of the manufacturing process is calculated using the second relationship. Based on the calculated evaluation function, a process specification that is an allowable value of the amount of particles generated in the manufacturing process is determined. in this way,
Manufacturing that maximizes the productivity of non-defective products in the entire manufacturing equipment by quantitatively evaluating both the yield improvement effect due to the maintenance of the manufacturing equipment and the decrease in productivity due to downtime due to the maintenance. It is possible to determine the particle management specifications of the process.

【0006】[0006]

【実施例】以下添付図面を参照して,本発明を具体化し
た実施例につき説明し,本発明の理解に供する。尚,以
下の実施例は,本発明を具体化した一例であって,本発
明の技術的範囲を限定する性格のものではない。ここ
に,図1は本発明の一実施例に係る工程スペック決定方
法の概略フローを示す図,図2は上記工程スペック決定
方法を適用可能な装置Aの概略構成を示すブロック図,
図3は装置使用累計時間とパーティクル発生数との関係
を示す図,図4はパーティクル発生数とメンテナンス頻
度との関係を示す図,図5はメンテナンス頻度と生産量
(試算値)との関係を示す図,図6はパーティクル発生
数と良品率(試算値)との関係を示す図,図7はパーテ
ィクル発生数と良品率・生産量との関係を示す図であ
る。図1に示す如く,本実施例に係る製品の製造工程に
おける工程スペック決定方法は,製品の製造工程で発生
するパーティクルの発生量とパーティクルによって阻害
される製品の良品率(歩留り)との対応関係を表す第1
の関係,およびパーティクルを低減するための製造工程
のメンテナンスの頻度と製品の生産量との対応関係を表
す第2の関係を予め記憶しておき(S1),記憶された
第1,第2の関係を用いて製造工程の生産性を評価する
ための評価関数を演算し(S2),演算された評価関数
に基づいて製造工程におけるパーティクルの発生量の許
容値である工程スペックを決定するように構成されてい
る。以下,この方法の基本原理について説明する。パー
ティクルの装置管理スペック,つまりパーティクルをど
の程度まで低減させれば,良品を生産するのに充分であ
るのかを決定するためには,以下のような手順で必要な
情報を取得すればよい。ある任意の工程における製造装
置のメンテナンス頻度,すなわちメンテナンスに係る時
間と稼働時間との比率と,製造装置のパーティクル発生
量との関係を実験的に求め,これから実際の場合を推測
する。任意の装置をiとし,その装置iのメンテナンス
頻度をXi,パーティクル発生量をPiと表現する。こ
こでメンテナンス頻度Xiとパーティクル発生量Piの
関係は1:1の対応関係であるため,実験結果を近似し
て数式Xi=fi(Pi)と表現する。近似手法として
は,例えば最小自乗法を用いればよい(以下同様)。製
造装置内の空気中に浮遊しているパーティクルの発生量
は市販のレーザパーティクルカウンタ等のパーティクル
測定器(不図示)により測定することができる。
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments embodying the present invention will be described below with reference to the accompanying drawings for the understanding of the present invention. The following embodiments are examples of embodying the present invention and are not intended to limit the technical scope of the present invention. 1 is a diagram showing a schematic flow of a process specification determining method according to an embodiment of the present invention, and FIG. 2 is a block diagram showing a schematic configuration of an apparatus A to which the process specification determining method can be applied.
3 is a diagram showing the relationship between the cumulative use time of the device and the number of particles generated, FIG. 4 is a diagram showing the relationship between the number of particles generated and the maintenance frequency, and FIG. 5 is a relationship between the maintenance frequency and the production amount (trial calculation value). 6 is a diagram showing the relationship between the number of particles generated and the non-defective product rate (trial calculation value), and FIG. 7 is a diagram showing the relationship between the number of particles generated and the non-defective product rate / production amount. As shown in FIG. 1, the process specification determining method in the manufacturing process of the product according to the present embodiment has a correspondence relationship between the generation amount of particles generated in the manufacturing process of the product and the non-defective rate (yield) of the product which is obstructed by the particles. First representing
And a second relationship representing a correspondence relationship between the maintenance frequency of the manufacturing process for reducing particles and the production amount of the product are stored in advance (S1), and the stored first and second relationships are stored. An evaluation function for evaluating the productivity of the manufacturing process is calculated using the relationship (S2), and the process specification, which is the allowable value of the amount of particles generated in the manufacturing process, is determined based on the calculated evaluation function. It is configured. The basic principle of this method will be described below. In order to determine the device management specification of particles, that is, how much particles should be reduced to produce a good product, necessary information may be acquired by the following procedure. The relation between the maintenance frequency of the manufacturing apparatus in a certain arbitrary process, that is, the ratio of the time related to maintenance to the operating time, and the particle generation amount of the manufacturing apparatus is experimentally obtained, and the actual case is inferred from this. Let i be an arbitrary device, Xi be the maintenance frequency of the device i, and Pi be the particle generation amount. Here, since the relationship between the maintenance frequency Xi and the particle generation amount Pi is 1: 1, the experimental result is approximated and expressed as mathematical expression Xi = fi (Pi). As an approximation method, for example, the least squares method may be used (the same applies below). The amount of particles floating in the air in the manufacturing apparatus can be measured by a particle measuring device (not shown) such as a commercially available laser particle counter.

【0007】次に,単位時間当たりの製品の生産量(工
程不良品を含む)をSと表現する。生産量Sは,メンテ
ナンス頻度Xiの値によって増減する量であることか
ら,この関係(第2の関係に相当)を試算し,関係式S
=s(Xi)を求める。この関係式は,データから近似
した数式でよい。次に,製品の良品率をRと表現し,良
品率Rと製造装置内のパーティクル発生量Piとの関係
(第1の関係に相当)を調査・解析を行うことにより,
実験的に求める。その実験データを近似することによ
り,関係式R=r(Pi)を求める。調査・解析は,実
際にある任意の工程で製造中の製品に付着したパーティ
クルを顕微鏡やそれに類する観察機器によって見つけだ
して,パーティクルの製品への付着の様子の観察結果か
ら不良を引き起こす割合を求める。さらに,この結果か
ら製品の歩留りを予測,試算する。ここで,製品の全製
造工程にわたり本方法によるスペック決定を行う場合を
考える。製造工程は複数の工程から成り立っており,あ
る1つの工程は他の工程に各関連している場合がほとん
どである。場合によっては,前の工程で製品に付着した
パーティクルは,次の工程で除去され,問題がなくなる
ことがある。そのような場合は,パーティクルが製品に
付着する工程と,次工程のパーティクル除去工程とを一
まとめにして,(パーティクル発生数)×(次工程での
パーティクル除去効率)をその工程におけるパーティク
ル発生量Piとすればよい。さらに説明を加えると,着
目した工程で製品に付着したパーティクルが,不良を引
き起こす確率を求めるには,実験的に装置内のパーティ
クルの発生量を変えて製品に強制的に付着させて,パー
ティクルの付着量と実際の歩留りとの関係を調査すれば
よい。
Next, the production amount of products per unit time (including defective products) is expressed as S. Since the production amount S is the amount that increases or decreases depending on the value of the maintenance frequency Xi, this relation (corresponding to the second relation) is trial calculated and the relational expression S
= S (Xi) is calculated. This relational expression may be a mathematical expression approximated from the data. Next, the good product rate of the product is expressed as R, and the relationship (corresponding to the first relationship) between the good product rate R and the particle generation amount Pi in the manufacturing apparatus is investigated and analyzed.
We ask experimentally. The relational expression R = r (Pi) is obtained by approximating the experimental data. In the investigation and analysis, the particles that actually adhere to the product being manufactured in an arbitrary process are found by a microscope or similar observation device, and the rate of causing defects is determined from the observation results of how the particles adhere to the product. Furthermore, the product yield is predicted and calculated from this result. Here, consider the case where the specification is determined by this method over the entire manufacturing process of the product. A manufacturing process consists of multiple processes, and one process is often related to another process. In some cases, the particles attached to the product in the previous process are removed in the next process, and the problem may disappear. In such a case, the process in which particles adhere to the product and the particle removal process in the next process are integrated, and (number of particles generated) x (particle removal efficiency in the next process) is the amount of particles generated in that process. It may be Pi. To further explain, in order to obtain the probability that particles attached to the product in the process of interest cause defects, the amount of particles generated in the equipment is changed experimentally to force the particles to adhere to the product, The relationship between the adhesion amount and the actual yield may be investigated.

【0008】以上のようにして,次のような関係式が導
出された。 Xi=fi(Pi) …(1) S=s(Xi) …(2) R=r(Pi) …(3) 上記(1)式を上記(2)式に代入することにより,生
産量(工程不良品を含む)Sは製造装置のパーティクル
発生量Piの関係式として表すことができる。 S=s(fi(Pi)) …(4) 良品の単位時間あたりの生産量としては,上記(3),
(4)式から次式が導出される。 S×R=s(fi(Pi))×r(Pi) …(5) 上記(5)式が良品の単位時間当たりの生産量を表わ
し,生産性(生産効率)を表わすパラメータ(評価関数
に相当)である。従って,上記(5)式により,パラメ
ータS×Rの値が最大となるようなパーティクル発生量
Piを求め,これを装置iの最適なパーティクル許容値
とすればよい。本方法はこのようにして,任意の装置i
のパーティクル発生量Piを変数にして,製品の良品
率,生産性を記述する関係式を導出することにより,最
適なパーティクルの許容値である工程スペックを求める
ことができる。引続いて,上記工程スペック決定方法を
より具体化するために該方法を適用可能な装置Aについ
て述べる。
As described above, the following relational expressions were derived. Xi = fi (Pi) (1) S = s (Xi) (2) R = r (Pi) (3) By substituting the equation (1) into the equation (2), the production amount ( S (including defective products) can be expressed as a relational expression of the particle generation amount Pi of the manufacturing apparatus. S = s (fi (Pi)) (4) As the production amount of non-defective products per unit time, the above (3),
The following equation is derived from the equation (4). S × R = s (fi (Pi)) × r (Pi) (5) The above equation (5) represents the production amount of the non-defective product per unit time, and is a parameter (in the evaluation function) that represents the productivity (production efficiency). Equivalent). Therefore, the particle generation amount Pi that maximizes the value of the parameter S × R is obtained from the above equation (5), and this may be set as the optimum particle allowable value of the apparatus i. The method thus proceeds in any device i
By using the particle generation amount Pi of 1 as a variable and deriving a relational expression that describes the non-defective rate and the productivity of the product, it is possible to obtain the process specification that is the optimum allowable value of the particles. Subsequently, in order to further embody the above-described process specification determination method, an apparatus A to which the method can be applied will be described.

【0009】図2に示す如く,本実施例に係る製品の製
造工程における工程スペック決定装置Aは,主として製
品の製造工程で発生するパーティクルの発生量とパーテ
ィクルによって阻害される上記製品の良品率(歩留り)
との対応関係を表す第1の関係を予め記憶しておくN
o.1記憶部1(第1の記憶手段に相当)と,パーティ
クルを低減するための製造工程のメンテナンスの頻度と
製品の生産量との対応関係を表す第2の関係を予め記憶
しておくNo.2記憶部2(第2の記憶手段に相当)
と,No.1,2記憶部1,2にそれぞれ記憶された第
1,第2の関係を用いて製造工程の生産性を評価するた
めの評価関数を演算する評価関数演算部3(評価関数演
算手段に相当)と,評価関数演算部3により演算された
評価関数に基づいて製造工程におけるパーティクルの発
生量の許容値である工程スペックを決定する工程スペッ
ク決定部4(工程スペック決定手段に相当)とから構成
されており,各構成要素は例えば図示しないコンピュー
タのソフトウエア上の各過程として構築されている。ま
た,本装置Aは,付属装置として上記第1,2の関係を
No.1,2記憶部1,2へ入力するためのキーボー
ド,ディスプレイ等からなる入力部0と,工程スペック
決定部4から結果を出力するためのプリンタ等からなる
出力部5とを備えている。従って,本装置Aでは,上記
工程スペック決定方法における各ステップの内,No.
1記憶部1およびNo.2記憶部2がステップS1を,
評価関数演算部3がステップS2を,工程スペック決定
部4がステップS3をそれぞれ実行する。以下,本装置
Aの具体的な動作例について説明する。即ち,本装置A
により,例えばHDD(ハードディスクドライブ)に用
いられる薄膜磁気ヘッドの製造工程の一工程である成膜
工程で使用する製造装置のパーティクル管理スペックの
決定を行う。ここでは,実験室で薄膜磁気ヘッドを実際
に試作した時のデータに基づいて説明する。
As shown in FIG. 2, the process specification determining apparatus A in the manufacturing process of the product according to the present embodiment mainly includes the generation amount of particles generated in the manufacturing process of the product and the non-defective rate of the product (). Yield)
The first relationship representing the correspondence relationship with
o. No. 1 storing unit 1 (corresponding to the first storing unit) and a second relation indicating the correspondence relation between the maintenance frequency of the manufacturing process for reducing particles and the production amount of the product are stored in advance. 2 storage unit 2 (corresponding to second storage means)
And No. An evaluation function calculation unit 3 (corresponding to evaluation function calculation means) that calculates an evaluation function for evaluating the productivity of the manufacturing process using the first and second relationships stored in the storage units 1 and 2, respectively. ) And a process spec determining unit 4 (corresponding to a process spec determining unit) that determines a process spec that is an allowable value of the amount of particles generated in the manufacturing process based on the evaluation function calculated by the evaluation function calculator 3. Each component is constructed as a process on software of a computer (not shown), for example. In addition, the present device A uses the No. 1 and No. 2 relationships described above as an auxiliary device. 1, 2 are provided with an input unit 0 such as a keyboard and a display for inputting to the storage units 1 and 2, and an output unit 5 such as a printer for outputting the result from the process specification determination unit 4. Therefore, in the present apparatus A, among the steps in the method of determining the process specification, No.
1 storage unit 1 and No. 1 2 The storage unit 2 executes step S1,
The evaluation function calculation unit 3 executes step S2, and the process specification determination unit 4 executes step S3. Hereinafter, a specific operation example of the device A will be described. That is, this device A
Thus, the particle management specifications of the manufacturing apparatus used in the film forming process, which is one of the manufacturing processes of the thin film magnetic head used for HDD (hard disk drive), are determined. Here, an explanation will be given based on the data obtained when a thin film magnetic head was actually prototyped in the laboratory.

【0010】先ず,スペック決定のための,ターゲット
となる実験装置をここではスパッタ成膜装置(i=1)
とする。この成膜装置のメンテナンス(装置クリーニン
グ)後のパーティクル発生数の数字を調べる。パーティ
クル発生数P1は市販のレーザパーティクルカウンタを
用いて調べた。その結果を図3に示す。図より,装置使
用累計時間の増大に伴い,パーティクル発生数P1が増
大する様子がわかる。次に,メンテナンスに費やす作業
時間を考慮して,図3の内容をパーティクル発生数P1
とメンテナンス頻度X1との関係に変換することにより
図4を求めた。また,ここでは,メンテナンス頻度=
(装置クリーニングに要する時間)×回数/(装置の稼
働時間)であるとし,また1回のメンテナンスの所要時
間を1時間,装置の稼働時間を10時間として前記
(1)式に代入することにより次式を求める。 X1=f1(P1)=60/P1 …(1′) 次に,単位時間あたりの生産量(工程不良品を含む)S
と,メンテナンス頻度X1との関係(第2の関係に相
当)を試算し,図5に表した。これを,前記(2)式に
代入することによって次の近似式を導出する。 S=s(X1)=10/X1 …(2′) オペレータは,この生産量(工程不良品を含む)Sとメ
ンテナンス頻度X1との関係を表す(2′)式を入力部
0を用いてNo.2記憶部2に入力する。次に,製品の
良品率Rと,成膜装置のパーティクル発生数P1との関
係(第1の関係に相当)を調査・解析することにより,
実験的に求めた結果を図6に示した。これを,前記
(3)式に代入することにより,次の近似式を導出す
る。 R=r(P1)=−0.0035×P1+1.75 …(3′) 調査・解析は,実際にある任意の工程で製品に付着した
パーティクルを顕微鏡やそれに類する観察機器により見
つけだし,パーティクルの製品への付着の様子から,不
良を引き起こす割合を求めた。オペレータは,この良品
率Rとパーティクル発生量P1との関係を表す(3′)
式を入力部0を用いてNo.1記憶部1に入力する。
First, an experimental apparatus as a target for determining specifications is a sputtering film forming apparatus (i = 1) here.
And The number of particles generated after the maintenance (device cleaning) of the film forming apparatus is checked. The number P1 of particles generated was examined using a commercially available laser particle counter. The result is shown in FIG. From the figure, it can be seen that the number P1 of particles generated increases with the increase in the cumulative use time of the apparatus. Next, considering the work time spent for maintenance, the content of FIG.
4 was obtained by converting the relationship between the maintenance frequency X1 and the maintenance frequency X1. In addition, here, maintenance frequency =
(Time required for device cleaning) × number of times / (device operating time), and by substituting into equation (1) the time required for one maintenance is 1 hour and the device operating time is 10 hours. Calculate the following formula. X1 = f1 (P1) = 60 / P1 (1 ') Next, the production amount per unit time (including process defective products) S
And the maintenance frequency X1 (corresponding to the second relationship) were calculated by trial calculation and shown in FIG. By substituting this into the equation (2), the following approximate equation is derived. S = s (X1) = 10 / X1 (2 ') The operator uses the input unit 0 to input the equation (2') showing the relationship between the production amount S (including defective products) and the maintenance frequency X1. No. 2 Input to the storage unit 2. Next, by investigating and analyzing the relationship (corresponding to the first relationship) between the product non-defective rate R and the particle generation number P1 of the film forming apparatus,
The result obtained experimentally is shown in FIG. The following approximate expression is derived by substituting this into the expression (3). R = r (P1) = − 0.0035 × P1 + 1.75 (3 ′) In the investigation / analysis, the particles adhering to the product in an actual arbitrary process are found by a microscope or similar observation device, and the product of the particle is obtained. The rate of causing defects was determined from the state of adhesion to the. The operator represents the relationship between the non-defective product rate R and the particle generation amount P1 (3 ').
No. is calculated using the input unit 0. 1 Input to the storage unit 1.

【0011】以上の作業終了後,オペレータの指示によ
り評価関数演算部3は,No.1,2記憶部1,2にそ
れぞれ記憶された内容に基づき以下の演算を行う。ここ
で上記(1′)式を上記(2′)式に代入することによ
り,生産量(工程不良品を含む)Sを,実験装置のパー
ティクル発生数P1の関係式として表わすことができ
る。 S=s(f1(P1))=10/X1=10/(60/P1)=P1/6 …(4′) 良品の単位時間当たりの生産量は,パラメータS×R
(評価関数に相当)で記述できるので,上記(3′),
(4′)式から次式が導かれる。この関係式を用いて評
価関数演算部3はパラメータS×Rの演算を行う。 S×R=s(f1(P1))×r(P1) =(P1×1/6)×(−0.0035×P1+1.75) =−0.00058×P12 +0.29×P1 …(5′) この関係式を図7に示した。図より,パラメータS×R
の値の最大値は,パーティクル発生数P1=250個の
ときであり,このときパラメータS×Rは36.25と
なる。よって,この場合,この成膜装置の最適なパーテ
ィクル管理スペックすなわち,工程スペックPaは,2
50個以下となる。このような工程スペックPaの決定
は工程スペック演算部4が行い,結果を出力部5から出
力する。以上より次のことがいえる。従来は製造装置内
に浮遊するパーティクル(ダスト)の製品への付着量の
測定が可能ではあるものの,パーティクルをどの程度ま
で低減させれば,良品を生産するのに充分であるのか,
また,メンテナンス作業の頻度は生産性を極力低下させ
ないという観点からどの程度が適当であるのかよくわか
っていなかった。しかし,本実施例の方法(装置A)に
よれば,製造装置のメンテナンスによる良品率(歩留
り)の向上効果とメンテナンスによるダウンタイムに伴
う生産性の低下との両方が定量的に評価できる。
After the above work is completed, the evaluation function operation unit 3 is operated by the operator's instruction. The following calculation is performed based on the contents stored in the storage units 1 and 2. By substituting the equation (1 ′) into the equation (2 ′), the production amount (including defective products) S can be expressed as a relational expression of the particle generation number P1 of the experimental apparatus. S = s (f1 (P1)) = 10 / X1 = 10 / (60 / P1) = P1 / 6 (4 ') The production amount of non-defective products per unit time is calculated by the parameter S × R.
(Corresponding to the evaluation function), the above (3 '),
The following equation is derived from the equation (4 '). The evaluation function calculator 3 calculates the parameter S × R using this relational expression. S × R = s (f1 (P1)) × r (P1) = (P1 × 1/6) × (−0.0035 × P1 + 1.75) = − 0.00058 × P1 2 + 0.29 × P1 ... ( 5 ') This relational expression is shown in FIG. From the figure, the parameter S × R
The maximum value of is when the number of generated particles P1 = 250, and the parameter S × R is 36.25 at this time. Therefore, in this case, the optimum particle management specification of this film forming apparatus, that is, the process specification Pa is 2
50 or less. The process specification calculation unit 4 determines the process specification Pa and outputs the result from the output unit 5. From the above, the following can be said. Conventionally, it is possible to measure the amount of particles (dust) floating in the manufacturing equipment attached to the product, but to what extent can the particles be reduced to produce good products?
In addition, it was not clear how appropriate the maintenance work frequency was from the viewpoint of minimizing productivity. However, according to the method (apparatus A) of the present embodiment, it is possible to quantitatively evaluate both the effect of improving the non-defective product rate (yield) due to the maintenance of the manufacturing apparatus and the decrease in productivity due to downtime due to the maintenance.

【0012】その結果,製造装置全体で良品の生産性を
最大化するような製造工程のパーティクル管理スペック
を決定し得る製品の製造工程における工程スペック決定
方法及びその装置を得ることができる。ところで,上記
実施例の装置Aは,最も基本的なものであり,上記
(1′)〜(3′)の各関係式を人間が予め求めておく
こととしたが,さらにこれらの式の導出をも含めること
も簡単にできる。具体的には,例えば評価関数演算部3
に導出手順を予め組み込んでおけばよい。その場合は,
入力データはパーティクル発生量Pi,メンテナンス頻
度Xi,生産量(工程不良品を含む)S等の基本データ
のみとなり,後は自動演算されるため,高速にかつ信頼
性に優れる工程スペックの決定を行うことができる。
尚,上記実施例の装置Aでは,各構成要素を例えばコン
ピュータのソフトウエア上の各過程として構築されるも
のと想定したが,実使用に際しては,それらの全体又は
一部をハードウエアにて構成しても何ら支障はない。
尚,上記実施例の装置Aの動作例では,スパッタ成膜装
置について説明したが実使用に際しては,他のあらゆる
超精密機械や集積回路等の製造に用いられる製造装置に
適用可能である。
As a result, it is possible to obtain a process specification determining method and apparatus in a product manufacturing process that can determine particle management specifications of a manufacturing process that maximizes the productivity of non-defective products in the entire manufacturing apparatus. By the way, the device A of the above-mentioned embodiment is the most basic one, and it is assumed that a human previously obtains the respective relational expressions (1 ′) to (3 ′). Can be easily included. Specifically, for example, the evaluation function calculation unit 3
The derivation procedure may be incorporated in advance. In that case,
The input data is only basic data such as particle generation amount Pi, maintenance frequency Xi, production amount (including process defective products) S, etc., and since it is automatically calculated after that, process specifications can be determined at high speed and with high reliability. be able to.
In the device A of the above embodiment, it is assumed that each component is constructed as, for example, each process in software of a computer, but in actual use, all or part of them is configured by hardware. However, there is no problem.
Incidentally, in the operation example of the apparatus A of the above-mentioned embodiment, the sputter film forming apparatus has been described, but in actual use, it can be applied to any other manufacturing apparatus used for manufacturing ultra-precision machines or integrated circuits.

【0013】[0013]

【発明の効果】本発明に係る製品の製造工程における工
程スペック決定方法及びその装置は,上記したように構
成されているため,製造装置のメンテナンスによる歩留
りの向上効果とメンテナンスによるダウンタイムに伴う
生産性の低下との両方が定量的に評価できる。その結
果,製造装置全体で良品の生産性を最大化するような製
造工程のパーティクル管理スペックを決定し得る製品の
製造工程における工程スペック決定方法及びその装置を
得ることができる。
Since the method and apparatus for determining the process specifications in the manufacturing process of the product according to the present invention are configured as described above, the effect of improving the yield due to the maintenance of the manufacturing device and the production accompanying the downtime due to the maintenance. Both the decrease in sex can be quantitatively evaluated. As a result, it is possible to obtain a process specification determining method and apparatus in a product manufacturing process that can determine particle management specifications in the manufacturing process that maximize the productivity of non-defective products in the entire manufacturing apparatus.

【図面の簡単な説明】[Brief description of drawings]

【図1】 本発明の一実施例に係る工程スペック決定方
法の概略フローを示す図。
FIG. 1 is a diagram showing a schematic flow of a process specification determining method according to an embodiment of the present invention.

【図2】 上記工程スペック決定方法を適用可能な装置
Aの概略構成を示すブロック図。
FIG. 2 is a block diagram showing a schematic configuration of an apparatus A to which the method for determining process specifications can be applied.

【図3】 装置使用累計時間とパーティクル発生数との
関係を示す図。
FIG. 3 is a diagram showing a relationship between a cumulative use time of the apparatus and the number of particles generated.

【図4】 パーティクル発生数とメンテナンス頻度との
関係を示す図。
FIG. 4 is a diagram showing the relationship between the number of particles generated and the maintenance frequency.

【図5】 メンテナンス頻度と生産量(試算値)との関
係を示す図。
FIG. 5 is a diagram showing a relationship between a maintenance frequency and a production amount (trial calculation value).

【図6】 パーティクル発生数と良品率(試算値)との
関係を示す図。
FIG. 6 is a diagram showing the relationship between the number of particles generated and the yield rate (trial calculation value).

【図7】 パーティクル発生数と良品率・生産量との関
係を示す図。
FIG. 7 is a diagram showing the relationship between the number of particles generated and the yield rate / production rate.

【符号の説明】[Explanation of symbols]

A…工程スペック決定装置 1…No.1記憶部(第1の記憶手段に相当) 2…No.2記憶部(第2の記憶手段に相当) 3…評価関数演算部(評価関数演算手段に相当) 4…工程スペック決定部(工程スペック決定手段に相
当)
A ... Process spec determination device 1 ... No. 1 storage unit (corresponding to a first storage unit) 2 ... No. 2 storage unit (corresponding to second storage unit) 3 ... evaluation function computing unit (corresponding to evaluation function computing unit) 4 ... process spec determining unit (corresponding to process spec determining unit)

Claims (2)

【特許請求の範囲】[Claims] 【請求項1】 製品の製造工程で発生するパーティクル
の発生量と該パーティクルによって阻害される上記製品
の歩留りとの対応関係を表す第1の関係,及び上記パー
ティクルを低減するための上記製造工程のメンテナンス
の頻度と上記製品の生産量との対応関係を表す第2の関
係を予め記憶しておき,上記記憶された第1,第2の関
係を用いて上記製造工程の生産性を評価するための評価
関数を演算し,上記演算された評価関数に基づいて上記
製造工程におけるパーティクルの発生量の許容値である
工程スペックを決定してなる製品の製造工程における工
程スペック決定方法。
1. A first relationship representing a correspondence relationship between an amount of particles generated in a manufacturing process of a product and a yield of the product which is hindered by the particles, and a manufacturing process of the manufacturing process for reducing the particles. A second relationship indicating the correspondence relationship between the maintenance frequency and the production amount of the product is stored in advance, and the productivity of the manufacturing process is evaluated using the stored first and second relationships. Is calculated, and a process specification which is an allowable value of the amount of particles generated in the manufacturing process is determined based on the calculated evaluation function.
【請求項2】 製品の製造工程で発生するパーティクル
の発生量と該パーティクルによって阻害される上記製品
の歩留りとの対応関係を表す第1の関係を予め記憶して
おく第1の記憶手段と,上記パーティクルを低減するた
めの上記製造工程のメンテナンスの頻度と上記製品の生
産量との対応関係を表す第2の関係を予め記憶しておく
第2の記憶手段と,上記第1,第2の記憶手段にそれぞ
れ記憶された第1,第2の関係を用いて上記製造工程の
生産性を評価するための評価関数を演算する評価関数演
算手段と,上記評価関数演算手段により演算された評価
関数に基づいて上記製造工程におけるパーティクルの発
生量の許容値である工程スペックを決定する工程スペッ
ク決定手段とを具備してなる製品の製造工程における工
程スペック決定装置。
2. A first storage means for storing in advance a first relationship representing a correspondence relationship between an amount of particles generated in a manufacturing process of a product and a yield of the product which is hindered by the particles. A second storage unit that stores in advance a second relationship indicating a correspondence relationship between the maintenance frequency of the manufacturing process for reducing the particles and the production amount of the product, and the first and second storage units. Evaluation function calculation means for calculating an evaluation function for evaluating the productivity of the manufacturing process using the first and second relationships stored in the storage means, and the evaluation function calculated by the evaluation function calculation means. And a process specification determining means for determining a process specification that is an allowable value of the amount of particles generated in the manufacturing process based on .
JP33095493A 1993-12-27 1993-12-27 Method and device for deciding process specification in production process of product Pending JPH07191732A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP33095493A JPH07191732A (en) 1993-12-27 1993-12-27 Method and device for deciding process specification in production process of product

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
JP33095493A JPH07191732A (en) 1993-12-27 1993-12-27 Method and device for deciding process specification in production process of product

Publications (1)

Publication Number Publication Date
JPH07191732A true JPH07191732A (en) 1995-07-28

Family

ID=18238260

Family Applications (1)

Application Number Title Priority Date Filing Date
JP33095493A Pending JPH07191732A (en) 1993-12-27 1993-12-27 Method and device for deciding process specification in production process of product

Country Status (1)

Country Link
JP (1) JPH07191732A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009140022A (en) * 2007-12-03 2009-06-25 Nippon Steel Corp Unit, method, and program for supporting facility maintenance plan generation, and computer readable recording medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009140022A (en) * 2007-12-03 2009-06-25 Nippon Steel Corp Unit, method, and program for supporting facility maintenance plan generation, and computer readable recording medium

Similar Documents

Publication Publication Date Title
CN106407085A (en) Performance monitoring method and apparatus
JP5827426B1 (en) Predictive diagnosis system and predictive diagnosis method
CN112823364A (en) Predictive model enhancement
WO2022091639A1 (en) Abnormality diagnosing model construction method, abnormality diagnosing method, abnormality diagnosing model construction device, and abnormality diagnosing device
CN100435052C (en) Working procedure management installation and controlling means thereof, procedure management program and recording medium thereof
JP2710568B2 (en) Production line management method
US7640131B2 (en) Data analysis method for analyzing failure root causes for products
US8340800B2 (en) Monitoring a process sector in a production facility
JP2002297217A (en) Quality management method in manufacture task, quality management supporting system and trend management program
US9851713B2 (en) Operation-time calculation device and method for calculating operation time
CN113834828A (en) Product quality analysis support system
JPH07191732A (en) Method and device for deciding process specification in production process of product
JPWO2019016892A1 (en) Quality analyzer and quality analysis method
JP3926478B2 (en) Semiconductor manufacturing method
JP7223947B2 (en) Manufacturing condition calculation device, manufacturing condition calculation method, and manufacturing condition calculation program
JP2003005822A (en) System for managing equipment
JPH0682353A (en) Method for measurement of fatigue crack propagation lower limit stress expansion coefficient range
JPH1049585A (en) Quality control method for product
JP4653526B2 (en) Quality analysis method, quality analysis apparatus, computer program, and computer-readable storage medium
JPS5930111A (en) Abnormality alarming system of production stage control
JP6947850B2 (en) Lot risk score-based dynamic lot measurement control method and system based on equipment confidence index
WO2023140196A1 (en) Abnormality determining method, and production control system
WO2023140195A1 (en) Abnormality determining method, and production control system
JPH01181186A (en) Quality deterioration supervising system
JPH05203551A (en) Method for testing fatigue crack propagation