本文選題：憶阻器 + 數(shù)字邏輯器件��； 參考：《西南大學》2017年碩士論文

【摘要】：隨著信息量的急劇增長和信息處理要求的不斷提高,人們迫切需要更加智能化和微型化的信息處理系統(tǒng),因此具有并行計算優(yōu)勢的神經(jīng)形態(tài)系統(tǒng)受到極大的關注。由于現(xiàn)有的半導體晶體管的尺寸無法進一步縮小,這使得與電子技術密切相關的神經(jīng)形態(tài)系統(tǒng)的研究受到嚴重限制。憶阻器具有類似于人類大腦的“記憶”功能,其納米級尺寸和非易失性存儲的特性,有望徹底改變現(xiàn)有的信息處理方式。本文將憶阻器應用到神經(jīng)網(wǎng)絡系統(tǒng)中,提出新一代的憶阻神經(jīng)網(wǎng)絡,該網(wǎng)絡能有效改善傳統(tǒng)神經(jīng)網(wǎng)絡電路復雜、不易集成的缺點,在降低能耗方面也表現(xiàn)出強大的潛力。被認為是天然電子突觸的憶阻器,能夠在仿生系統(tǒng)里得到完美應用,讓憶阻神經(jīng)網(wǎng)絡變得更加的靈活。本文深入研究了憶阻器特性,并在此基礎上來構建新型憶阻神經(jīng)網(wǎng)絡電路。討論了純憶阻邏輯電路,并構建了憶阻數(shù)字邏輯器件;將憶阻器和neuMOS晶體管相結合,提出了新型憶阻離散Hopfield神經(jīng)網(wǎng)絡,并研究了其在彩色數(shù)字圖像恢復中的應用;構建了一種參數(shù)自適應的新型憶阻脈沖耦合神經(jīng)網(wǎng)絡,并提出了一種圖像增強自適應算法。具體來說,本文內容主要分為四個部分,如下所示:首先,本文重點討論了經(jīng)典的惠普憶阻器模型和閾值自適應模型,探討了憶阻值與電荷、磁通量三者之間的關系。利用SPICE仿真驗證了該模型的憶阻特性,并重點研究了該模型的閾值特性和突觸特性,為憶阻器后續(xù)應用研究提供良好的理論參考和實驗依據(jù)。然后,本文基于惠普憶阻器的邏輯計算能力和信息存儲特性,設計了純憶阻邏輯電路。不同于傳統(tǒng)的憶阻邏輯電路,本文提出的電路用電壓來直接表示邏輯狀態(tài),更加直觀方便。相比于晶體管邏輯電路,則在電路復雜程度上有明顯的改善。在此基礎上,本文構建了憶阻編碼器和憶阻譯碼器,仿真驗證了其邏輯的正確性。該方案推進憶阻器在數(shù)字電路中的應用,為優(yōu)化邏輯器件提供了新的思路。其次,利用神經(jīng)元晶體管的加權求和特性以及閾值可控功能,結合憶阻器的突觸特性,提出了一種全新的憶阻Hopfield神經(jīng)網(wǎng)絡,并將其運用在聯(lián)想記憶和彩色數(shù)字圖像恢復中。該網(wǎng)絡僅由neuMOS、憶阻器和普通電阻構成,能夠完全模擬神經(jīng)元信息傳導過程,相比傳統(tǒng)電路,省去了復雜的差分運算電路以及電流與電壓信號的轉換電路,電路結構簡單,可用于大規(guī)模集成。同時,該網(wǎng)絡還具有能耗低、閾值動態(tài)可控、權值可編程的優(yōu)點�？梢�,該方案不僅極大地簡化網(wǎng)絡結構,還能加強網(wǎng)絡性能,有助于促進人工神經(jīng)形態(tài)系統(tǒng)的硬件實現(xiàn)。最后,本文將憶阻器和傳統(tǒng)PCNN模型相結合,提出了一種基于閾值自適應憶阻器的M-PCNN神經(jīng)元模型。模型中用憶阻器電路的輸出來模擬神經(jīng)元間的連接強度,實現(xiàn)實際情況中神經(jīng)元間的連接強度隨外部刺激自適應動態(tài)變化的過程。這種全新的神經(jīng)元模型擴展了神經(jīng)網(wǎng)絡的動態(tài)特性,為參數(shù)自適應神經(jīng)網(wǎng)絡的發(fā)展提供了一個新的思路。進一步,本文提出了一種基于M-PCNN神經(jīng)元模型的自適應圖像增強算法,從人眼視覺主觀特性和客觀性能評價指標兩方面證明了該算法的優(yōu)越性。該算法能突出圖像局部細節(jié),明暗對比更顯著,為進一步促進神經(jīng)網(wǎng)絡在圖像處理中的應用和發(fā)展奠定了基礎。
[Abstract]:With the explosion of information and information processing requirements continue to increase, there is an urgent need for information processing system more intelligent and miniaturization, so it has the computational advantage of parallel neural morphological systems has attracted much attention. Due to the size of the existing semiconductor transistor cannot be further reduced, which makes the research of neuromorphic system closely related to electronic technology the limits. The memristor is similar to the human brain memory function, the nanometer size and non easy characteristics of nonvolatile storage, is expected to completely change the existing mode of information processing. In this paper the memristor is applied to the neural network system, a new generation of memristive neural network. This network can effectively improve the traditional neural network circuit is complex and not easy to integrate the disadvantages in terms of reducing energy consumption shows great potential. Considered natural electronic process The memristor touch, can obtain perfect application in biomimetic system, let memristive neural network become more flexible. This paper studies the memristor characteristics, and on this foundation to construct the model of memristor neural network circuit is discussed. The pure memristor logic circuit, and the construction of the memristor digital logic device; the the memristor and neuMOS transistor combination, put forward the new memristor discrete Hopfield neural network, and discussed its application in color digital image restoration; constructed a model of memristor parameter adaptive pulse coupled neural network, and put forward a kind of adaptive image enhancement algorithm. Specifically, the main content of this article four parts as follows: firstly, this paper discusses the classical HP memristor model and adaptive threshold model, discusses the relationship between memory resistance and charge flux is three. By SPICE simulation test The characteristic of the memristor model, and focuses on the threshold characteristics and synaptic characteristics of the model, providing theoretical and experimental evidence for good memristor subsequent application research. Then, the computing power and information storage characteristics of HP memristor based on the logic design, the pure memristor logic circuit. In the memristor traditional logic circuit, this voltage to said logic state, more intuitive and convenient. Compared to the transistor logic circuit, it has obvious improvement in circuit complexity. On this basis, this paper constructs the memristor encoder and decoder of memristor simulation, verified the correctness of the logic. The scheme to promote the application of memristor in digital circuit, provides a new idea for the optimization of logic devices. Secondly, using the weighted sum of the neuron transistor characteristics and threshold controllable function, combined with the memristor Synaptic characteristics, proposed a new memristor Hopfield neural network and its application in the recovery of associative memory and color digital image. The network only by neuMOS, a memristor and ordinary resistance, can fully simulate the process of neuronal information transmission, compared with the traditional circuit, eliminating the need for complex difference operation circuit, current and voltage signal conversion circuit, the circuit structure is simple, can be used for large-scale integration. At the same time, the network also has the advantages of low energy consumption, dynamic threshold controllable, the advantages of programmable weights. Obviously, this scheme not only greatly simplify network structure, can enhance network performance, help to promote the form of artificial neural system the hardware implementation. Finally, the memristor and the combination of the traditional PCNN model, proposes a M-PCNN neuron model with adaptive threshold based on memristor. Output model with the memristor circuit to die The connection strength between neurons, the connection strength between neurons in the actual situation with the external stimulus. The dynamic changes of the adaptive neuron model extends the dynamic characteristics of the new neural network, provides a new idea for the development of adaptive neural network. Further, this paper proposes an adaptive image enhancement algorithm the M-PCNN neuron model based on human visual subjective and objective characteristics of two aspects of performance evaluation index to prove the superiority of the algorithm. The algorithm can enhance the image details, the contrast is more significant, which lays a foundation for the further promotion of the development and application of neural network in image processing.

【學位授予單位】：西南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP183

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