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蔡佳麟 & 陳怡婷 - 袂當無你..mp4
陳怡婷 & 李嘉 - 愛我三分鐘.mp4
交流賽 郭婷筠 & 陳怡婷-一個人的心事.mp4
53 #91等待舞伴.MP4
52 #90自作多情.MP4
51 #89風中的蠟燭.mp4
50 #88放捨你我心嘸甘.mp4
49 #87將我放心肝.mp4
48 #86明知已經無緣.mp4
47 #85我愛的人不是愛我的人.mp4
46 #84手下留情.mp4
45 #83愛到坎站.mp4
44 #82看破的夢.mp4
43 #81圍巾.mp4
42 #80一個人愛過一個人.wmv
41 #79悲情歌聲.mp4
40 #78愛到抹凍愛.mp4
39 #77約束.mp4
38 #76感情變負擔.flv
37#75 找無你.flv
36 #74咖啡.wmv_(HQ).mp4
35 #73心狠手辣 .mp4
34 #73啞吧情歌.mp4
33 #72請借問心愛的人.mp4
32 #72失戀雨.mp4
31 #71生死戀mp4.mp4
30 #71咱的一生咱的愛mp4.mp4
29 #70不顧一切.flv
28 #70愛人這薄倖.flv
27 #69落雨聲.flv
26#69 我的所在我的人.flv
25 #68RADIO的點歌心情.flv
24 #68原來你不識愛過我.flv
23#67 衛冕賽-人生的歌
22#67 資格賽-傷心的所在
21#66 衛冕賽-女人的故事
20#66 資格賽-傷心夜都市
19#65 衛冕賽-風醉雨也醉
18#65 資格賽-想起伊
17#64 衛冕賽-無情人請你離開
16#64 資格賽-半包菸
15#63 衛冕賽-野草亦是花
14#63 資格賽-孤單酒
13#62 資格賽-不通將阮放
12#61 資格賽-愛到這為止
11#60 資格賽-愛你愛甲心痛
10#59 衛冕賽-收不回的愛
9#59 資格賽-伴阮過一生
8#58 資格賽-感謝無謝人
7#57 資格賽-苦是無塊換
藝人交流:演唱-愛你的記號
6#25 資格賽-雨傘情
5#24 衛冕賽-傷心第四台
4#24 資格賽-借問你的良心何在
3#23 資格賽-愛你無條件
#13 藝人交流:與偉中合唱-情深深
2#14 資格賽-感情放一邊
1#13 資格賽-苦酒若喉
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好聽啦:-D
常見的半導體材料有矽、鍺、砷化鎵等
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晶片測試
晶片處理高度有序化的本質增加了對不同處理步驟之間度量方法的需求。晶片測試度量裝置被用於檢驗晶片仍然完好且沒有被前面的處理步驟損壞。如果If the number of dies—the 積體電路s that will eventually become chips—當一塊晶片測量失敗次數超過一個預先設定的閾值時,晶片將被廢棄而非繼續後續的處理製程。
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晶片測試
晶片處理高度有序化的本質增加了對不同處理步驟之間度量方法的需求。晶片測試度量裝置被用於檢驗晶片仍然完好且沒有被前面的處理步驟損壞。如果If the number of dies—the 積體電路s that will eventually become chips—當一塊晶片測量失敗次數超過一個預先設定的閾值時,晶片將被廢棄而非繼續後續的處理製程。
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步驟列表
晶片處理
濕洗
平版照相術
光刻Litho
離子移植IMP
蝕刻(干法蝕刻、濕法蝕刻、電漿蝕刻)
熱處理
快速熱退火Annel
熔爐退火
熱氧化
化學氣相沉積 (CVD)
物理氣相沉積 (PVD)
分子束磊晶 (MBE)
電化學沉積 (ECD),見電鍍
化學機械平坦化 (CMP)
IC Assembly and Testing 封裝測試
Wafer Testing 晶片測試
Visual Inspection外觀檢測
Wafer Probing電性測試
FrontEnd 封裝前段
Wafer BackGrinding 晶背研磨
Wafer Mount晶圓附膜
Wafer Sawing晶圓切割
Die attachment上片覆晶
Wire bonding焊線
BackEnd 封裝後段
Molding模壓
Post Mold Cure後固化
De-Junk 去節
Plating 電鍍
Marking 列印
Trimform 成形
Lead Scan 檢腳
Final Test 終測
Electrical Test電性測試
Visual Inspection光學測試
Baking 烘烤
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有害材料標誌
許多有毒材料在製造過程中被使用。這些包括:
有毒元素摻雜物比如砷、硼、銻和磷
有毒化合物比如砷化三氫、磷化氫和矽烷
易反應液體、例如過氧化氫、發煙硝酸、硫酸以及氫氟酸
工人直接暴露在這些有毒物質下是致命的。通常IC製造業高度自動化能幫助降低暴露於這一類物品的風險。
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Device yield
Device yield or die yield is the number of working chips or dies on a wafer, given in percentage since the number of chips on a wafer (Die per wafer, DPW) can vary depending on the chips' size and the wafer's diameter. Yield degradation is a reduction in yield, which historically was mainly caused by dust particles, however since the 1990s, yield degradation is mainly caused by process variation, the process itself and by the tools used in chip manufacturing, although dust still remains a problem in many older fabs. Dust particles have an increasing effect on yield as feature sizes are shrunk with newer processes. Automation and the use of mini environments inside of production equipment, FOUPs and SMIFs have enabled a reduction in defects caused by dust particles. Device yield must be kept high to reduce the selling price of the working chips since working chips have to pay for those chips that failed, and to reduce the cost of wafer processing. Yield can also be affected by the design and operation of the fab.
Tight control over contaminants and the production process are necessary to increase yield. Contaminants may be chemical contaminants or be dust particles. "Killer defects" are those caused by dust particles that cause complete failure of the device (such as a transistor). There are also harmless defects. A particle needs to be 1/5 the size of a feature to cause a killer defect. So if a feature is 100 nm across, a particle only needs to be 20 nm across to cause a killer defect. Electrostatic electricity can also affect yield adversely. Chemical contaminants or impurities include heavy metals such as Iron, Copper, Nickel, Zinc, Chromium, Gold, Mercury and Silver, alkali metals such as Sodium, Potassium and Lithium, and elements such as Aluminum, Magnesium, Calcium, Chlorine, Sulfur, Carbon, and Fluorine. It is important for those elements to not remain in contact with the silicon, as they could reduce yield. Chemical mixtures may be used to remove those elements from the silicon; different mixtures are effective against different elements.
Several models are used to estimate yield. Those are Murphy's model, Poisson's model, the binomial model, Moore's model and Seeds' model. There is no universal model; a model has to be chosen based on actual yield distribution (the location of defective chips) For example, Murphy's model assumes that yield loss occurs more at the edges of the wafer (non-working chips are concentrated on the edges of the wafer), Poisson's model assumes that defective dies are spread relatively evenly across the wafer, and Seeds's model assumes that defective dies are clustered together.[25]
Smaller dies cost less to produce (since more fit on a wafer, and wafers are processed and priced as a whole), and can help achieve higher yields since smaller dies have a lower chance of having a defect. However, smaller dies require smaller features to achieve the same functions of larger dies or surpass them, and smaller features require reduced process variation and increased purity (reduced contamination) to maintain high yields. Metrology tools are used to inspect the wafers during the production process and predict yield, so wafers predicted to have too many defects may be scrapped to save on processing costs.[26]